ARA-C01 SnowPro Advanced: Architect Certification Exam Questions and Answers

According to the Snowflake documentation, unstructured data files can be shared by using a secure view and Secure Data Sharing. A secure view allows the result of a query to be accessed like a table, and a secure view is specifically designated for data privacy. A scoped URL is an encoded URL that permits temporary access to a staged file without granting privileges to the stage. The URL expires when the persisted query result period ends, which is currently 24 hours. A scoped URL is recommended for file administrators to give scoped access to data files to specific roles in the same account. Snowflake records information in the query history about who uses a scoped URL to access a file, and when. Therefore, a scoped URL is the best option to share unstructured data within Snowflake, as it provides security, accountability, and control over the data access. References:

Sharing unstructured Data with a secure view

Introduction to Loading Unstructured Data

Option B is the best design to meet the requirements because it uses Snowpipe to ingest the data continuously and efficiently as new records arrive in the object storage, leveraging event notifications. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables1. It also uses streams and tasks to orchestrate transformations on the ingested data. Streams are objects that store the change history of a table, and tasks are objects that execute SQL statements on a schedule or when triggered by another task2. Option B also uses an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. An external function is a user-defined function that calls an external API, such as Amazon Comprehend, to perform computations that are not natively supported by Snowflake3. Finally, option B uses the Snowflake Marketplace to make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions. The Snowflake Marketplace is a platform that enables data providers to list and share their data sets with data consumers, regardless of the cloud platform or region they use4.

Option A is not the best design because it uses copy into to ingest the data, which is not as efficient and continuous as Snowpipe. Copy into is a SQL command that loads data from files into a table in a single transaction. It also exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.

Option C is not the best design because it uses Amazon EMR and PySpark to ingest and transform the data, which also increases the operational complexity and maintenance of the infrastructure. Amazon EMR is a cloud service that provides a managed Hadoop framework to process and analyze large-scale data sets. PySpark is a Python API for Spark, a distributed computing framework that can run on Hadoop. Option C also develops a python program to do model inference by leveraging the Amazon Comprehend text analysis API, which increases the development effort.

Option D is not the best design because it is identical to option A, except for the ingestion method. It still exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.

To ensure that ananalyst_usercan only access Snowflake from specific IP addresses, the following steps are required:

Option B: This alters the network policy directly linked toanalyst_user. Setting a network policy on the user level is effective and ensures that the specified network restrictions apply directly and exclusively to this user.

Option D: Before a network policy can be set or altered, the appropriate role with permission to manage network policies must be used.SECURITYADMINis typically the role that has privileges to create and manage network policies in Snowflake. Creating a network policy that specifies allowed IP addresses ensures that only requests coming from those IPs can access Snowflake under this policy. After creation, this policy can be linked to specific users or roles as needed.

Options A and E mention altering roles or using the wrong role (USERADMINtypically does not manage network security settings), and option C incorrectly attempts to set a network policy directly as an IP address, which is not syntactically or functionally valid.

Snowpark workloads are often memory- and compute-intensive, especially when executing complex transformations, large joins, or machine learning logic inside stored procedures. In Snowflake, the MAX_CONCURRENCY_LEVEL warehouse parameter controls how many concurrent queries can run on a single cluster of a virtual warehouse. Lowering concurrency increases the amount of compute and memory available to each individual query.

Setting MAX_CONCURRENCY_LEVEL = 1 ensures that only one query can execute at a time on the warehouse cluster, allowing that query to consume the maximum possible share of CPU, memory, and I/O resources. This is the recommended configuration when the goal is to optimize performance for a single Snowpark job rather than maximizing throughput for many users. Higher concurrency levels would divide resources across multiple queries, reducing per-query performance and potentially causing spilling to remote storage.

For SnowPro Architect candidates, this question reinforces an important cost and performance tradeoff: concurrency tuning is a powerful lever. When running batch-oriented or compute-heavy Snowpark workloads, architects should favor lower concurrency to maximize per-query resources, even if that means fewer concurrent workloads.

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QUESTION NO: 12 [Cost Control and Resource Management]

An Architect executes the following query:

SELECT query_hash,

COUNT(*) AS query_count,

SUM(QH.EXECUTION_TIME) AS total_execution_time,

SUM((QH.EXECUTION_TIME / (1000 * 60 * 60)) * 8) AS c

FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY QH

WHERE warehouse_name = 'WH_L'

AND DATE_TRUNC('day', start_time) >= CURRENT_DATE() - 3

GROUP BY query_hash

ORDER BY c DESC

LIMIT 10;

What information does this query provide? (Select TWO).

A. It shows the total execution time and credit estimates for the 10 most expensive individual queries executed on WH_L over the last 3 days.

B. It shows the total execution time and credit estimates for the 10 most expensive query groups (identical or similar queries) executed on WH_L over the last 3 days.

C. It shows the total execution time and credit estimates for the 10 most frequently run query groups executed on WH_L over the last 3 days.

D. It calculates relative cost by converting execution time to minutes and multiplying by credits used.

E. It calculates relative cost by converting execution time to hours and multiplying by credits used.

Answer: B, E

This query groups results by QUERY_HASH, which represents logically identical SQL statements. As a result, the aggregation is performed at the query group level, not at the individual execution level. This allows architects to identify patterns where the same query (or same logical SQL) repeatedly consumes a large amount of compute (Answer B).

The cost calculation converts execution time from milliseconds to hours by dividing by (1000 * 60 * 60) and then multiplies the result by 8, which represents the hourly credit consumption of the WH_L warehouse size. This provides a relative estimate of credit usage per query group, not an exact billing value but a useful approximation for cost analysis (Answer E).

The query does not identify the most frequently executed queries; although COUNT(*) is included, the ordering is done by calculated cost (c), not by frequency. This type of analysis is directly aligned with SnowPro Architect responsibilities, helping architects optimize workloads, refactor expensive query patterns, and right-size warehouses to control costs.

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QUESTION NO: 13 [Architecting Snowflake Solutions]

An Architect is designing a disaster recovery plan for a global fraud reporting system. The plan must support near real-time systems using Snowflake data, operate near regional centers with fully redundant failover, and must not be publicly accessible.

Which steps must the Architect take? (Select THREE).

A. Create multiple replicating Snowflake Standard edition accounts.

B. Establish one Snowflake account using a Business Critical edition or higher.

C. Establish multiple Snowflake accounts in each required region with independent data sets.

D. Set up Secure Data Sharing among all Snowflake accounts in the organization.

E. Create a Snowflake connection object.

F. Create a failover group for the fraud data for each regional account.

Answer: B, C, F

Mission-critical, near real-time systems with strict availability and security requirements require advanced Snowflake features. Business Critical edition (or higher) is required to support failover groups and cross-region replication with higher SLA guarantees and compliance capabilities (Answer B). To meet regional proximity and redundancy requirements, multiple Snowflake accounts must be deployed in each required region, ensuring independence and isolation between regional environments (Answer C).

Failover groups are the core Snowflake mechanism for disaster recovery. They replicate selected databases, schemas, and roles across accounts and allow controlled promotion of secondary accounts to primary during failover events (Answer F). Secure Data Sharing alone does not provide DR or replication, and connection objects are unrelated to availability or redundancy.

This design aligns with SnowPro Architect best practices for multi-region disaster recovery, enabling low-latency regional access, controlled failover, and strong isolation without exposing systems to the public internet.

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QUESTION NO: 14 [Snowflake Data Engineering]

What transformations are supported in the following SQL statement? (Select THREE).

CREATE PIPE … AS

COPY INTO …

FROM ( … )

A. Data can be filtered by an optional WHERE clause.

B. Columns can be reordered.

C. Columns can be omitted.

D. Type casts are supported.

E. Incoming data can be joined with other tables.

F. The ON_ERROR = ABORT_STATEMENT command can be used.

Answer: A, B, D

Snowflake’s COPY INTO statement (including when used with Snowpipe) supports a limited but useful set of transformations. Data can be filtered using a WHERE clause when loading from a staged SELECT statement, enabling simple row-level filtering (Answer A). Columns can also be reordered by explicitly selecting fields in a different order than they appear in the source (Answer B). Additionally, type casting is supported, allowing raw data to be cast into target column data types during ingestion (Answer D).

However, COPY INTO does not support joins with other tables; it is designed for ingestion, not complex transformations. Columns can be omitted implicitly by not selecting them, but this is not considered a transformation feature in the context of Snowpipe exam questions. The ON_ERROR option is an error-handling configuration, not a transformation.

SnowPro Architect candidates are expected to recognize that COPY INTO and Snowpipe are ingestion-focused tools. More complex transformations should be handled downstream using streams and tasks, dynamic tables, or transformation frameworks like dbt.

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QUESTION NO: 15 [Security and Access Management]

A company wants to share selected product and sales tables with global partners. The partners are not Snowflake customers but do have access to AWS.

Requirements:

Data access must be governed.

Each partner should only have access to data from its respective region.What is the MOST secure and cost-effective solution?

A. Create reader accounts and share custom secure views.

B. Create an outbound share and share custom secure views.

C. Export secure views to each partner’s Amazon S3 bucket.

D. Publish secure views on the Snowflake Marketplace.

Answer: A

When sharing data with partners who are not Snowflake customers, Snowflake reader accounts provide the most secure and cost-effective solution. Reader accounts allow data providers to host and govern access within their own Snowflake environment while allowing consumers to query shared data without owning a Snowflake account (Answer A). This ensures strong governance, centralized billing, and no data movement.

By sharing custom secure views, the company can enforce row-level and column-level security so that each partner only sees data from its authorized region. Outbound shares require the consumer to have their own Snowflake account, which is not the case here. Exporting data to S3 introduces unnecessary data duplication, security risk, and operational overhead. Snowflake Marketplace is designed for broad distribution, not partner-specific regional restrictions.

For the SnowPro Architect exam, this question highlights best practices in secure data sharing, governance, and cost control when collaborating with external, non-Snowflake partners.

When a SnowflakeBusiness Critical (BC)edition account shares data, it must followdata sharing restrictionsdesigned to maintain thehigher level of compliance and securityguaranteed by BC.

Bydefault, BC accounts canonly share data with other BC (or higher)accounts, to maintain consistent security and compliance (e.g., HIPAA, HITRUST, FedRAMP).

However,an exceptioncan be madeif a user with the proper privilegeexplicitly disables the restriction.

Key Concept: share_restrictions

Snowflake enforcesdata sharing restrictionsby default forBC accounts.

Arole with the OVERRIDE SHARE RESTRICTIONS global privilegecan bypass this by setting theshare_restrictions = FALSEwhen adding the target account.

Correct Option: D

This is correct because:

The usermust have a role with the OVERRIDE SHARE RESTRICTIONS privilege.

That user can thenset share_restrictions = FALSEwhen adding the Enterprise edition consumer account.

Official Documentation Extract:

"If the data provider is a Business Critical (or higher) account, Snowflake enforces a restriction by default that only allows sharing data with other Business Critical (or higher) accounts. A user in the provider account with a role that has the global privilege OVERRIDE SHARE RESTRICTIONS can override this restriction by explicitly setting SHARE_RESTRICTIONS = FALSE when adding the consumer account."

Source:Snowflake Docs – CREATE SHARE

Why Other Options Are Incorrect:

A.Incorrect – This is not absolute. Business Critical accountscanshare data with Enterprise accounts,if the restriction is explicitly overridden.

B.Incorrect – Simply having authority to create or alter a share isnot enough. You must have the OVERRIDE SHARE RESTRICTIONS privilege and set the restriction explicitly.

C.Incorrect – Setting share_restrictions = FALSE is required, but theprivilege to overridemust also be held by the role. Without the privilege, the action will fail.

By default, Snowflake does not allow sharing data from a Business Critical edition to a non-Business Critical edition. This is because Business Critical edition provides enhanced security and data protection features that are not available in lower editions. However, this restriction can be overridden by setting the share_restriction parameter on the shared object (database, schema, or table) to false. This parameter allows the data provider to explicitly allow sharing data with lower edition accounts. Note that this parameter can only be set by the data provider, not the data consumer. Also, setting this parameter to false may reduce the level of security and data protection for the shared data.

Enable Data Share:Business Critical Account to Lower Edition

Sharing Is Not Allowed From An Account on BUSINESS CRITICAL Edition to an Account On A Lower Edition

SQL Execution Error: Sharing is Not Allowed from an Account on BUSINESS CRITICAL Edition to an Account on a Lower Edition

Snowflake Editions | Snowflake Documentation

This question tests understanding of Snowflake Role-Based Access Control (RBAC) and privilege inheritance, which is a core SnowPro Architect exam topic. In Snowflake, privileges are not granted directly to users; instead, they are granted to roles, which are then assigned to users. Effective access depends on the combination of USAGE and SELECT privileges across databases, schemas, and tables.

In the provided design, the role DB_IT_RO_ROLE has SELECT privileges on all tables in the database, along with database usage. This role is granted to IT_ANALYST_ROLE, which is assigned to User1. As a result, User1 can read tables from both IT_SCHEMA and FINANCE_SCHEMA, assuming schema usage is satisfied, which is implied in the diagram.

User2, on the other hand, is granted the FIN_ANALYST_ROLE, which inherits privileges from DB_FIN_ROLE. That role only has USAGE and SELECT privileges on the FINANCE_SCHEMA. There are no grants that allow DB_FIN_ROLE (or FIN_ANALYST_ROLE) to access objects in IT_SCHEMA.

Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported. This could be caused by the following factors:

The order of the keys in the JSON was changed. Snowflake stores semi-structured data internally in a column-like structure for the most common elements, and the remainder in a leftovers-like column. The order of the keys in the JSON affects how Snowflake determines the common elements and how it optimizes the query performance. If the order of the keys in the JSON was changed, Snowflake might have to re-parse the data and re-organize the internal storage, which could result in slower query performance.

There were variations in string lengths for the JSON values in the recent data imports. Non-native values, such as dates and timestamps, are stored as strings when loaded into a VARIANT column. Operations on these values could be slower and also consume more space than when stored in a relational column with the corresponding data type. If there were variations in string lengths for the JSON values in the recent data imports, Snowflake might have to allocate more space and perform more conversions, which could also result in slower query performance.

The other options are not valid causes for poor query performance:

There were JSON nulls in the recent data imports. Snowflake supports two types of null values in semi-structured data: SQL NULL and JSON null. SQL NULL means the value is missing or unknown, while JSON null means the value is explicitly set to null. Snowflake can distinguish between these two types of null values and handle them accordingly. Having JSON nulls in the recent data imports should not affect the query performance significantly.

The recent data imports contained fewer fields than usual. Snowflake can handle semi-structured data with varying schemas and fields. Having fewer fields than usual in the recent data imports should not affect the query performance significantly, as Snowflake can still optimize the data ingestion and query execution based on the existing fields.

Considerations for Semi-structured Data Stored in VARIANT

Snowflake Architect Training

Snowflake query performance on unique element in variant column

Snowflake variant performance

Snowflake virtual warehouses maintain a local result and data cache only while the warehouse is running. When a warehouse is suspended—whether manually or via auto-suspend—the local cache is cleared. As a result, subsequent queries cannot benefit from cached data and must re-scan data from remote storage, leading to slower execution (Answer D).

Snowflake does maintain a global result cache at the cloud services layer, but it is only used when the exact same query text is re-executed and the underlying data has not changed. In many analytical workloads, queries vary slightly, preventing reuse of the result cache.

Warehouse size and multi-cluster configuration impact concurrency and throughput, not cache persistence. There is no USE_CACHE parameter in Snowflake. This question tests an architect’s understanding of Snowflake caching behavior and the tradeoff between aggressive auto-suspend for cost control and cache reuse for performance.

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QUESTION NO: 32 [Security and Access Management]

A company has two databases, DB1 and DB2.

Role R1 has SELECT on DB1.

Role R2 has SELECT on DB2.

Users should normally access only one database, but a small group must access both databases in the same query with minimal operational overhead.

What is the best approach?

A. Set DEFAULT_SECONDARY_ROLE to R2.

B. Grant R2 to users and use USE_SECONDARY_ROLES for SELECT.

C. Grant R2 to R1 to use privilege inheritance.

D. Grant R2 to users and require USE SECONDARY ROLES.

Answer: B

Snowflake supports secondary roles to allow users to activate additional privileges without changing their primary role. Granting R2 to the users and enabling USE_SECONDARY_ROLES for SELECT allows those users to access both DB1 and DB2 in a single query, while keeping their default role unchanged (Answer B).

This approach minimizes operational overhead because it avoids role restructuring or privilege inheritance changes. It also maintains least privilege by ensuring that users only activate additional access when needed. Setting a default secondary role applies automatically and may unintentionally broaden access. Granting R2 to R1 affects all users with R1, which violates the requirement to limit access to a small group.

This pattern is a common SnowPro Architect design for cross-database access control.

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QUESTION NO: 33 [Performance Optimization and Monitoring]

How can an Architect enable optimal clustering to enhance performance for different access paths on a given table?

A. Create multiple clustering keys for a table.

B. Create multiple materialized views with different cluster keys.

C. Create super projections that automatically create clustering.

D. Create a clustering key containing all access path columns.

Answer: B

Snowflake allows only one clustering key per table, which limits its effectiveness when multiple access paths exist. Creating a composite clustering key that includes many columns often leads to poor clustering depth and limited pruning.

Materialized views provide an effective alternative. Each materialized view can be clustered independently, allowing architects to tailor physical data organization to specific query patterns (Answer B). Queries targeting different access paths can then leverage the appropriate materialized view, achieving better pruning and performance.

Super projections are not a Snowflake feature. Creating multiple clustering keys on a single table is not supported. This question reinforces SnowPro Architect knowledge of advanced performance design techniques using materialized views.

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QUESTION NO: 34 [Cost Control and Resource Management]

An Architect configures the following timeouts and creates a task using a size X-Small warehouse. The task’s INSERT statement will take ~40 hours.

How long will the INSERT execute?

A. 1 minute

B. 5 minutes

C. 1 hour

D. 40 hours

Answer: A

Tasks in Snowflake are governed by the USER_TASK_TIMEOUT_MS parameter, which specifies the maximum execution time for a single task run. In this scenario, USER_TASK_TIMEOUT_MS = 60000, which equals 1 minute. This timeout applies regardless of account-, session-, or warehouse-level statement timeout settings.

Even though the account, session, and warehouse statement timeouts are higher, the task-specific timeout takes precedence for task execution. As a result, the INSERT statement will be terminated after 1 minute (Answer A).

This is a key SnowPro Architect concept: tasks have their own execution limits that override other timeout parameters. Architects must ensure that task timeouts are configured appropriately for long-running operations or redesign workloads to fit within task constraints.

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QUESTION NO: 35 [Snowflake Ecosystem and Integrations]

Several in-house applications need to connect to Snowflake without browser access or redirect capabilities.

What is the Snowflake best practice for authentication?

A. Use Snowflake OAuth.

B. Use usernames and passwords.

C. Use external OAuth.

D. Use key pair authentication with a service user.

Answer: D

For non-interactive, service-to-service authentication scenarios, Snowflake recommends key pair authentication using a service user (Answer D). This method avoids hardcoding passwords, supports automated rotation of credentials, and aligns with security best practices.

OAuth-based methods typically require browser redirects or user interaction, which are not available in this scenario. Username/password authentication introduces security risks and operational overhead.

Key pair authentication enables strong, certificate-based security and is widely used in SnowPro Architect designs for applications, ETL tools, and automated workloads.

The VALIDATION_MODE parameter is used to specify the behavior of the COPY statement when loading data into a table. It is used to specify whether the COPY statement should return an error if any of the rows in the file are invalid or if it should continue loading the valid rows. The VALIDATION_MODE parameter is only supported for COPY statements that load data from external stages1.

The query in the question uses a data transformation query to load data from an internal stage. A data transformation query is a query that transforms the data during the load process, such as parsing JSON or XML data, applying functions, or joining with other tables2.

According to the documentation, VALIDATION_MODE does not support COPY statements that transform data during a load. If the parameter is specified, the COPY statement returns an error1. Therefore, option C is the correct answer.

 COPY INTO <title> : Transforming Data During a Load

The Snowflake SQL REST API is a REST API that you can use to access and update data in a Snowflake database. You can use this API to execute standard queries and most DDL and DML statements. This API can be used to develop custom applications and integrations that can read and write data to Snowflake without installing any additional software on the application server. Option A is not correct because SnowSQL is a command-line client that requires installation and configuration on the application server. Option B is not correct because the Snowpipe REST API is used to load data from cloud storage into Snowflake tables, not to read or write data to Snowflake. Option D is not correct because the Snowflake ODBC driver is a software component that enables applications to connect to Snowflake using the ODBC protocol, which also requires installation and configuration on the application server. References: The answer can be verified from Snowflake’s official documentation on the Snowflake SQL REST API available on their website. Here are some relevant links:

Snowflake SQL REST API | Snowflake Documentation

Introduction to the SQL API | Snowflake Documentation

Submitting a Request to Execute SQL Statements | Snowflake Documentation

 Option C is the correct answer because it allows the company to share data privately with the customer across different cloud platforms and regions. The company can create a new Snowflake account in Azure East US 2 (Virginia) and set up replication between AWS us-west-2 (Oregon) and Azure East US 2 (Virginia) for the database objects to be shared. This way, the company can ensure that the data is always up to date and consistent in both accounts. The company can then create a share and add the database privileges to the share, and alter the share and add the customer’s Snowflake account to the share. The customer can then access the shared data from their own Snowflake account in Azure East US 2 (Virginia).

Option A is incorrect because the Snowflake Marketplace is not a private way of sharing data. The Snowflake Marketplace is a public data exchange platform that allows anyone to browse and subscribe to data sets from various providers. The company would not be able to control who can access their data if they use the Snowflake Marketplace.

Option B is incorrect because it requires the customer to create a new Snowflake account in Azure East US 2 (Virginia), which may not be feasible or desirable for the customer. The customer may already have an existing Snowflake account in a different cloud platform or region, and may not want to incur additional costs or complexity by creating a new account.

Option D is incorrect because it involves creating a reader account in Azure East US 2 (Virginia), which is a limited and temporary way of sharing data. A reader account is a special type of Snowflake account that can only access data from a single share, and has a fixed duration of 30 days. The company would have to manage the reader account’s URL and credentials, and renew the account every 30 days. The customer would not be able to use their own Snowflake account to access the shared data, and would have to rely on the company’s reader account.

This command will fail because while the user has USAGE permission ondb2andschema2through Role B, and can create a view inschema2, they do not have SELECT permission ondb1.schemal.table1with Role B. Since Role A, which has SELECT permission ondb1.schemal.table1, is not the currently active role when the viewv2is being created indb2.schema2, the user does not have the necessary permissions to read fromdb1.schemal.table1to create the view. Snowflake’s security model requires that the active role have all necessary permissions to execute the command.

To provide the highest uptime and least application disruption in case of a service event, the best option is to use the Business Critical Snowflake edition and connect the applications using the -<accountLocator> URL. The Business Critical Snowflake edition offers the highest level of security, performance, and availability for Snowflake accounts. It includes features such as customer-managed encryption keys, HIPAA compliance, and 4-hour RPO and RTO SLAs. It also supports account replication and failover across regions and cloud platforms, which enables business continuity and disaster recovery. By using the -<accountLocator> URL, the applications can leverage the Snowflake Client Redirect feature, which automatically redirects the client connections to the secondary account in case of a failover. This way, the applications can seamlessly switch to the backup account without any manual intervention or configuration changes. The other options are less cost-effective or less reliable because they either use a lower edition of Snowflake, which does not support account replication and failover, or they use the - URL, which does not support client redirect and requires manual updates to the connection string in case of a failover. References:

[Snowflake Editions] 1

[Replication and Failover/Failback] 2

[Client Redirect] 3

[Snowflake Account Identifiers] 4

Client Redirect is a Snowflake feature designed to support business continuity and disaster recovery in multi-account, multi-region architectures. When configured, it allows client applications to automatically connect to a secondary Snowflake account if the primary account becomes unavailable. This secondary account must be part of a configured failover group and promoted to act as the primary during a failover event.

The correct behavior of Client Redirect is to redirect client connections transparently to another Snowflake account—typically in a different region—where replicated data and objects are available and the account is acting as the primary (Answer D). This minimizes downtime and avoids the need for manual connection string updates across applications.

The feature does not simply provide alternate login URLs, nor does it dynamically route queries across multiple active accounts. It also does not automatically select the “closest” region; redirection follows the configured failover topology. For SnowPro Architect candidates, this question emphasizes understanding Snowflake’s native disaster recovery mechanisms and how Client Redirect integrates with failover groups.

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QUESTION NO: 47 [Security and Access Management]

A call center processes sensitive PII and PHI data. Requirements:

Object owners and privileged roles must see sensitive columns.

Pre-tokenized data must be de-tokenized at query time.What should be used? (Select TWO).

A. Object tagging

B. Materialized views

C. External tokenization

D. Dynamic Data Masking

E. Role-Based Access Control (RBAC)

Answer: D, E

Dynamic Data Masking is a Snowflake security feature that conditionally masks or reveals column values at query time based on the executing role (Answer D). This allows privileged users (such as object owners or compliance roles) to see clear-text PII/PHI, while other users see masked or tokenized values. Because masking is evaluated at query time, it supports de-tokenization logic when the role is authorized.

Role-Based Access Control (RBAC) underpins this behavior by defining which roles are privileged and allowed to view unmasked data (Answer E). Masking policies are evaluated in the context of the active role, making RBAC essential for enforcing correct access.

Object tagging alone does not enforce access control. Materialized views duplicate data and do not support dynamic de-tokenization logic. External tokenization is not required when Snowflake masking policies can handle conditional reveal. This question tests SnowPro Architect knowledge of advanced column-level security and privacy controls.

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QUESTION NO: 48

(Skipped — no Question 48 in the original set)

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QUESTION NO: 49 [Snowflake Data Engineering]

A table contacts_data contains a VARIANT column with nested JSON data.

Which queries correctly retrieve the first name value "Snow"? (Select TWO).

A. SELECT o[customer][name][first]::STRING FROM contacts_data;

B. SELECT o['customer']['name']['first']::STRING FROM contacts_data;

C. SELECT o:['customer']:['name']:['first']::STRING FROM contacts_data;

D. SELECT o:Customer.Name.First::STRING FROM contacts_data;

E. SELECT o:customer.name.first::STRING FROM contacts_data;

Answer: B, E

Snowflake supports multiple syntaxes for accessing elements in VARIANT columns. Bracket notation with quoted keys is valid for object traversal, making option B correct. Dot notation with colon syntax is also valid and commonly used for readability, as shown in option E.

Option A is invalid because unquoted identifiers inside brackets are interpreted as column names, not JSON keys. Option C contains incorrect syntax combining brackets and colons. Option D is invalid because JSON key names are case-sensitive unless quoted, and the provided structure uses lowercase keys.

Understanding VARIANT traversal syntax is a core SnowPro Architect skill for semi-structured data handling and transformation.

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QUESTION NO: 50 [Cost Control and Resource Management]

An Architect is evaluating compute and storage costs related to performance optimization techniques.

Which Snowflake performance optimizations have storage costs associated with them? (Select THREE).

A. Rekeying

B. Materialized views

C. Clustering a table

D. Query Acceleration Service

E. Search Optimization Service

F. Multi-cluster virtual warehouse

Answer: B, C, E

Some Snowflake performance features require additional storage to maintain auxiliary data structures. Materialized views store precomputed query results and therefore consume storage (Answer B). Clustering a table can increase storage usage due to additional micro-partitions created during reclustering operations (Answer C).

Search Optimization Service builds and maintains search access paths that persist as additional metadata structures, incurring storage costs (Answer E). In contrast, Query Acceleration Service and multi-cluster warehouses affect compute usage only. Rekeying is a security operation and does not create separate storage structures.

This question tests SnowPro Architect understanding of cost tradeoffs between compute and storage when selecting optimization techniques.

 A masking policy is a feature of Snowflake that allows masking sensitive data in query results based on the role of the user and the condition of the data. A masking policy can be applied to a column in a table or a view, and it can use another column in the same table or view as a conditional column. A conditional column is a column that determines whether the masking policy is applied or not based on its value1.

In this case, the requirement can be met by using a masking policy on the username column with event_timestamp as a conditional column. The masking policy can use a function that masks the username if the event_timestamp is older than six months based on the current date, and returns the original username otherwise. The masking policy canbe applied to the user_events table, and it will also be applied when creating views using the table or cloning the table2.

The other options are not correct because:

A. Using a masking policy on the username column using an entitlement table with valid dates would require creating another table that stores the valid dates for each username, and joining it with the user_events table in the masking policy function. This would add complexity and overhead to the masking policy, and it would not use the event_timestamp column as the condition for masking.

B. Using a row level policy on the user_events table using an entitlement table with valid dates would require creating another table that stores the valid dates for each username, and joining it with the user_events table in the row access policy function. This would filter out the rows that have event_timestamp older than six months based on the valid dates, instead of masking the username column. This would not meet the requirement of masking the username, and it would also reduce the visibility of the event data.

D. Using a secure view on the user_events table using a case statement on the username column would require creating a view that uses a case expression to mask the username column based on the event_timestamp column. This would meet the requirement of masking the username, but it would not be applied when cloning the table. A secure view is a view that prevents the underlying data from being exposed by queries on the view. However, a secure view does not prevent the underlying data from being exposed by cloning the table3.

1: Masking Policies | Snowflake Documentation

2: Using Conditional Columns in Masking Policies | Snowflake Documentation

3: Secure Views | Snowflake Documentation

Warehouse credits are used to pay for the processing time used by each virtual warehouse in Snowflake. A virtual warehouse is a cluster of compute resources that enables executing queries, loading data, and performing other DML operations. Warehouse credits are charged based on the number of virtual warehouses you use, how long they run, and their size1.

Among the commands listed in the question, the following ones will use warehouse credits:

SELECT MAX(FLAKE_ID) FROM SNOWFLAKE: This command will use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the maximum value of the FLAKE_ID column2. Therefore, option B is correct.

SELECT COUNT(*) FROM SNOWFLAKE: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows in the table3. Therefore, option C is correct.

SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows for each distinct value of the FLAKE_ID column4. Therefore, option D is correct.

The command that will not use warehouse credits is:

SHOW TABLES LIKE ‘SNOWFL%’: This command will not use warehouse credits because it is a metadata operation that does not require a virtual warehouse to execute. The command will return the names of the tables that match the pattern ‘SNOWFL%’ in the current database and schema5. Therefore, option A is incorrect.

 Understanding Compute Cost : MAX Function : COUNT Function : GROUP BY Clause : SHOW TABLES

 Snowflake Connector for Kafka and Snowpipe are two ingestion methods that can be used to load near real-time data by using the messaging services provided by a cloud provider. Snowflake Connector for Kafka enables you to stream structured and semi-structured data from Apache Kafka topics into Snowflake tables. Snowpipe enables you to load data from files that are continuously added to a cloud storage location, such as Amazon S3 or Azure Blob Storage. Both methods leverage Snowflake’s micro-partitioning and columnar storage to optimize data ingestion and query performance. Snowflake streams and Spark are not ingestion methods, but rather components of the Snowflake architecture. Snowflake streams provide change data capture (CDC) functionality by tracking data changes in a table. Spark is a distributed computing framework that can be used to process large-scale data and write it to Snowflake using the Snowflake Spark Connector. References:

Snowflake Connector for Kafka

Snowpipe

Snowflake Streams

Snowflake Spark Connector

 To maximize memory and compute resources for a Snowpark stored procedure, you need to set the MAX_CONCURRENCY_LEVEL parameter for the warehouse that executes the stored procedure. This parameter determines the maximum number of concurrent queries that can run on a single warehouse. By setting it to 16, you ensure that the warehouse can use all the available CPU cores and memory on a single node, which is the optimal configuration for Snowpark-optimized warehouses. This will improve the performance and efficiency of the stored procedure, as it will not have to share resources with other queries or nodes. The other options are incorrect because they either do not change the MAX_CONCURRENCY_LEVEL parameter, or they set it to a lower value than 16, which will reduce the memory and compute resources for the stored procedure. References:

[Snowpark-optimized Warehouses] 1

[Training Machine Learning Models with Snowpark Python] 2

[Snowflake Shorts: Snowpark Optimized Warehouses] 3

 Snowflake context functions are functions that return information about the current session, user, role, warehouse, database, schema, or object. They can be used to help determine whether a user is authorized to see data that has column-level security enforced by setting masking policy conditions based on the context functions. The following context functions are relevant for column-level security:

current_role: This function returns the name of the role in use for the current session. It can be used to set masking policy conditions that target the current session and are not affected by the execution context of the SQL statement. For example, a masking policy condition using current_role can allow or deny access to a column based on the role that the user activated in the session.

invoker_role: This function returns the name of the executing role in a SQL statement. It can be used to set masking policy conditions that target the executing role and are affected by the execution context of the SQL statement. For example, a masking policy condition using invoker_role can allow or deny access to a column based on the role that the user specified in the SQL statement, such as using the AS ROLE clause or a stored procedure.

is_role_in_session: This function returns TRUE if the user’s current role in the session (i.e. the role returned by current_role) inherits the privileges of the specified role. It can be used to set masking policy conditions that involve role hierarchy and privilege inheritance. For example, a masking policy condition using is_role_in_session can allow or deny access to a column based on whether the user’s current role is a lower privilege role in the specified role hierarchy.

The other options are not valid ways to use the Snowflake context functions for column-level security:

Set masking policy conditions using is_role_in_session targeting the role in use for the current account. This option is incorrect because is_role_in_session does not target the role in use for the current account, but rather the role in use for the current session. Also, the current account is not a role, but rather a logical entity that contains users, roles, warehouses, databases, and other objects.

Determine if there are ownership privileges on the masking policy that would allow the use of any function. This option is incorrect because ownership privileges on the masking policy do not affect the use of any function, but rather the ability to create, alter, or drop the masking policy. Also, this is not a way to use the Snowflake context functions, but rather a way to check the privileges on the masking policy object.

Assign the accountadmin role to the user who is executing the object. This option is incorrect because assigning the accountadmin role to the user who is executing the object does not involve using the Snowflake context functions, but rather granting the highest-level role to the user. Also, this is not a recommended practice for column-level security, as it would give the user full access to all objects and data in the account, which could compromise data security and governance.

Context Functions

Advanced Column-level Security topics

Snowflake Data Governance: Column Level Security Overview

Data Security Snowflake Part 2 - Column Level Security

In the context of business intelligence (BI) queries, which are typically focused on data analysis and reporting, the star schema is the most suitable data modeling approach.

Option B: Star Schema- The star schema is a type of relational database schema that is widely used for developing data warehouses and data marts for BI purposes. It consists of a central fact table surrounded by dimension tables. The fact table contains the core data metrics, and the dimension tables contain descriptive attributes related to the fact data. The simplicity of the star schema allows for efficient querying and aggregation, which are common operations in BI reporting.

This is the valid object hierarchy when building a Snowflake environment, according to the Snowflake documentation and the web search results. Snowflake is a cloud data platform that supports various types of objects, such as databases, schemas, tables, views, stages, warehouses, and more. These objects are organized in a hierarchical structure, as follows:

Organization: An organization is the top-level entity that represents a group of Snowflake accounts that are related by business needs or ownership. An organization can have one or more accounts, and can enable features such as cross-account data sharing, billing and usage reporting, and single sign-on across accounts12.

Account: An account is the primary entity that represents a Snowflake customer. An account can have one or more databases, schemas, stages, warehouses, and other objects. An account can also have one or more users, roles, and security integrations. An account is associated with a specific cloud platform, region, and Snowflake edition34.

Database: A database is a logical grouping of schemas. A database can have one or more schemas, and can store structured, semi-structured, or unstructured data. A database can also have properties such as retention time, encryption, and ownership56.

Schema: A schema is a logical grouping of tables, views, stages, and other objects. A schema can have one or more objects, and can define the namespace and access control for the objects. A schema can also have properties such as ownership and default warehouse .

Stage: A stage is a named location that references the files in external or internal storage. A stage can be used to load data into Snowflake tables using the COPY INTO command, or to unload data from Snowflake tables using the COPY INTO LOCATION command. A stage can be created at the account, database, or schema level, and can have properties such as file format, encryption, and credentials .

The other options listed are not valid object hierarchies, because they either omit or misplace some objects in the structure. For example, option A omits the organization level and places the warehouse under the schema level, which is incorrect. Option C omits the organization, account, and stage levels, and places the table under the schema level, which is incorrect. Option D omits the database level and places the stage and table under the account level, which is incorrect.

Snowflake Documentation: Organizations

Snowflake Blog: Introducing Organizations in Snowflake

Snowflake Documentation: Accounts

Snowflake Blog: Understanding Snowflake Account Structures

Snowflake Documentation: Databases

Snowflake Blog: How to Create a Database in Snowflake

[Snowflake Documentation: Schemas]

[Snowflake Blog: How to Create a Schema in Snowflake]

[Snowflake Documentation: Stages]

[Snowflake Blog: How to Use Stages in Snowflake]

 A standard virtual warehouse policy is one of the two scaling policies available for multi-cluster warehouses in Snowflake. The other policy is economic. A standard policy aims to prevent or minimize queuing by starting additional clusters as soon as the current cluster is fully loaded, regardless of the number of queries in the queue. This policy can improve query performance and concurrency, but it may also consume more credits than an economic policy, which tries to conserve credits by keeping the running clusters fully loaded before starting additional clusters. The scaling policy can be set when creating or modifying a warehouse, and it can be changed at any time.

Snowflake Documentation: Multi-cluster Warehouses

Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses

The architecture in the image shows a Snowflake data platform with two databases, DB1 and DB2, and two schemas, SH1 and SH2. DB1 contains a table TBL1 and a stage STAGE1. DB2 contains a table TBL2. The image also shows a snippet of code written in SQL language that copies data from STAGE1 to TBL2 using a file format FF PIPE 1.

To copy data from DB1 to TBL2, there are two possible options among the choices given:

Option B: Use a named external stage that references STAGE1. This option requires creating an external stage object in DB2.SH2 that points to the same location as STAGE1 in DB1.SH1. The external stage can be created using the CREATE STAGE command with the URL parameter specifying the location of STAGE11. For example:

SQLAI-generated code. Review and use carefully. More info on FAQ.

use database DB2;

use schema SH2;

createstage EXT_STAGE1

url=@DB1.SH1.STAGE1;

Then, the data can be copied from the external stage to TBL2 using the COPY INTO command with the FROM parameter specifying the external stage name and the FILE FORMAT parameter specifying the file format name2. For example:

SQLAI-generated code. Review and use carefully. More info on FAQ.

copyintoTBL2

from@EXT_STAGE1

file format=(format name=DB1.SH1.FF PIPE1);

Option E: Use a cross-database query to select data from TBL1 and insert into TBL2. This option requires using the INSERT INTO command with the SELECT clause to query data from TBL1 in DB1.SH1 and insert it into TBL2 in DB2.SH2. The query must use the fully-qualified names of the tables, including the database and schema names3. For example:

SQLAI-generated code. Review and use carefully. More info on FAQ.

use database DB2;

use schema SH2;

insertintoTBL2

select*fromDB1.SH1.TBL1;

The other options are not valid because:

Option A: It uses an invalid syntax for the COPY INTO command. The FROM parameter cannot specify a table name, only a stage name or a file location2.

Option C: It uses an invalid syntax for the COPY INTO command. The FILE FORMAT parameter cannot specify a stage name, only a file format name or options2.

Option D: It uses an invalid syntax for the CREATE STAGE command. The URL parameter cannot specify a table name, only a file location1.

1: CREATE STAGE | Snowflake Documentation

2: COPY INTO table | Snowflake Documentation

3: Cross-database Queries | Snowflake Documentation

Tri-Secret Secure is only available in Snowflake Business Critical edition or higher, making that edition mandatory to satisfy the first requirement (Answer B). Secure views are required when sharing data with another Snowflake customer to ensure that underlying table data, logic, and metadata are protected and governed during Secure Data Sharing (Answer C). To hide or reveal portions of sensitive data at query time based on role or policy, Dynamic Data Masking is the appropriate Snowflake feature (Answer E).

Row access policies control row-level visibility but do not mask column values. The Enterprise edition does not support Tri-Secret Secure, making it insufficient for the stated requirements. Materialized views are unrelated to security or data masking and introduce unnecessary storage overhead.

This question integrates multiple SnowPro Architect domains—security, governance, sharing, and environment management—and tests the architect’s ability to select the minimal set of Snowflake features that collectively meet complex compliance and operational requirements.

=========

QUESTION NO: 52 [Snowflake Data Engineering]

Which columns can be included in an external table schema? (Select THREE).

A. VALUE

B. METADATA$ROW_ID

C. METADATA$ISUPDATE

D. METADATA$FILENAME

E. METADATA$FILE_ROW_NUMBER

F. METADATA$EXTERNAL_TABLE_PARTITION

Answer: A, D, E

External tables in Snowflake expose a combination of user-defined columns and system-generated metadata columns. The VALUE column is commonly used to store semi-structured data (such as JSON or Avro records) read directly from external storage (Answer A).

Snowflake also provides metadata columns that describe the source file. METADATA$FILENAME identifies the name of the file from which a given row was read (Answer D), and METADATA$FILE_ROW_NUMBER indicates the row number within that file (Answer E). These columns are frequently used for auditing, debugging, and data lineage tracking.

METADATA$ROW_ID and METADATA$ISUPDATE are associated with streams and change tracking, not external tables. METADATA$EXTERNAL_TABLE_PARTITION is not a valid selectable column in the external table schema definition. This question reinforces SnowPro Architect knowledge of how Snowflake represents external data and exposes file-level metadata for data lake architectures.

=========

QUESTION NO: 53 [Security and Access Management]

An Architect needs to ensure that users can upload data from Snowsight into an existing table.

What privileges must be granted? (Select THREE).

A. Database: USAGE

B. Database: OWNERSHIP

C. Schema: CREATE TABLE

D. Schema: USAGE

E. Table: SELECT

F. Table: OWNERSHIP

Answer: A, D, E

Uploading data into an existing table via Snowsight requires sufficient privileges to access the database and schema and to interact with the target table. Database-level USAGE is required to access objects within the database (Answer A). Schema-level USAGE is required to access the schema containing the table (Answer D).

Table-level SELECT is required by Snowsight to validate and preview the data and table structure during the upload process (Answer E). OWNERSHIP privileges are not required and would grant excessive control. CREATE TABLE is unnecessary when uploading into an existing table.

This reflects Snowflake’s least-privilege security model and is a common SnowPro Architect exam topic when designing user self-service data ingestion workflows.

=========

QUESTION NO: 54 [Performance Optimization and Monitoring]

An Architect is troubleshooting a long-running statement and needs to identify blocked transactions and the queries blocking them.

Which views should be used? (Select TWO).

A. QUERY_HISTORY

B. OBJECT_DEPENDENCIES

C. DATA_TRANSFER_HISTORY

D. LOCK_WAIT_HISTORY

E. ACCESS_HISTORY

Answer: A, D

LOCK_WAIT_HISTORY provides detailed information about transactions waiting on locks, including which transactions are blocked and which ones are blocking them (Answer D). This view is essential for diagnosing contention and concurrency issues.

QUERY_HISTORY complements this by providing execution details about the blocking queries, such as duration, user, and SQL text (Answer A). Together, these views allow architects to correlate blocked transactions with the responsible workloads.

The other views are unrelated to transaction locking behavior. This question highlights SnowPro Architect troubleshooting skills related to concurrency and transaction management.

=========

QUESTION NO: 55 [Snowflake Ecosystem and Integrations]

Which functions does the Data Build Tool (dbt) facilitate? (Select TWO).

A. Data loading

B. Data testing

C. Data visualization

D. Data transformation

E. Data replication

Answer: B, D

dbt is a transformation-focused analytics engineering tool that operates inside the data warehouse. It enables SQL-based data transformations and manages dependencies between models (Answer D). dbt also provides built-in data testing capabilities, allowing teams to define and run tests for data quality, such as uniqueness, not-null constraints, and referential integrity checks (Answer B).

dbt does not handle data loading, visualization, or replication; those functions are typically handled by ingestion tools, BI platforms, or replication services. SnowPro Architect candidates are expected to understand dbt’s role in the Snowflake ecosystem and how it fits into modern ELT architectures.

In Snowflake's parameter hierarchy, virtual warehouse parameters take precedence over user parameters. This hierarchy is designed to ensure that settings at the virtual warehouse level, which typically reflect the requirements of a specific workload or set of queries, override the preferences set at the individual user level. This helps maintain consistent performance and resource utilization as specified by the administrators managing the virtual warehouses.

Snowflake supports data sharing across regions and cloud platforms using account replication and share replication features. Account replication enables the replication of objects from a source account to one or more target accounts in the same organization. Share replication enables the replication of shares from a source account to one or more target accounts in the same organization1.

To share data from the MARKET_DB database in the ACCOUNTA account in AWS us-east-1 region with the PARTNERB account in Azure East US 2 region, the following steps must be performed:

Create a new account (called AZABC123) in Azure East US 2 region. This account will act as a bridge between the source and the target accounts. The new account must be linked to the ACCOUNTA account using an organization2.

From the ACCOUNTA account, replicate the MARKET_DB database to the AZABC123 account using the account replication feature. This will create a secondary database in the AZABC123 account that is a replica of the primary database in the ACCOUNTA account3.

From the AZABC123 account, set up the data sharing to the PARTNERB account using the share replication feature. This will create a share of the secondary database in the AZABC123 account and grant access to the PARTNERB account. The PARTNERB account can then create a database from the share and query the data4.

Therefore, option C is the correct answer.

 Replicating Shares Across Regions and Cloud Platforms : Working with Organizations and Accounts : Replicating Databases Across Multiple Accounts : Replicating Shares Across Multiple Accounts

To accommodate the diverse needs of different teams and use cases within a company, a flexible and multi-faceted approach to data modeling is required.

Option B:By creating a raw database for landing and persisting raw data, you ensure that the Data Science team has access to unprocessed data for machine learning model development. This aligns with the best practices of having a staging area or raw data zone in a modern data architecture where raw data is ingested before being transformed or processed for different use cases.

Option C:Having profile-specific databases means creating targeted databases that are designed to meet the specific requirements of each user profile or team within the company. For the Finance and Vendor Management teams, the data can be structured and optimized for reporting and visualization. For the Sales team, the database can include engineered and protected data that is suitable for data monetization efforts. This strategy not only aligns data with usage patterns but also helps in managing data access and security policies effectively.

 Option A is the best design to meet the requirements because it uses a materialized view on top of an external table against the S3 bucket in AWS Singapore. A materialized view is a database object that contains the results of a query and can be refreshed periodically to reflect changes in the underlying data1. An external table is a table that references data files stored in a cloud storage service, such as Amazon S32. By using a materialized view on top of an external table, the company can provide access to frequently changing data, keep egress costs to a minimum, and maintain low latency. This is because the materialized view will cache the query results in Snowflake, reducing the need to access the external data files and incur network charges. The materialized view will also improve the query performance by avoiding scanning the external data files every time. The materialized view can be refreshed on a schedule or on demand to capture the changes in the external data files1.

Option B is not the best design because it uses an external table against the S3 bucket in AWS Singapore and copies the data into transient tables. A transient table is a tablethat is not subject to the Time Travel and Fail-safe features of Snowflake, and is automatically purged after a period of time3. By using an external table and copying the data into transient tables, the company will incur more egress costs and operational overhead than using a materialized view. This is because the external table will access the external data files every time a query is executed, and the copy operation will also transfer data from S3 to Snowflake. The transient tables will also consume more storage space in Snowflake and require manual maintenance to ensure they are up to date.

Option C is not the best design because it copies the data between providers from S3 to Azure Blob storage to collocate, then uses Snowpipe for data ingestion. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables4. By copying the data between providers, the company will incur high egress costs and latency, as well as operational complexity and maintenance of the infrastructure. Snowpipe will also add another layer of processing and storage in Snowflake, which may not be necessary if the external data files are already in a queryable format.

Option D is not the best design because it uses AWS Transfer Family to replicate data between the S3 bucket in AWS Singapore and an Azure Netherlands Blob storage, then uses an external table against the Blob storage. AWS Transfer Family is a service that enables secure and seamless transfer of files over SFTP, FTPS, and FTP to and from Amazon S3 or Amazon EFS5. By using AWS Transfer Family, the company will incur high egress costs and latency, as well as operational complexity and maintenance of the infrastructure. The external table will also access the external data files every time a query is executed, which may affect the query performance.

In a managed access schema, the privilege management is centralized with the schema owner, who has the authority to grant object privileges within the schema. Additionally, the SECURITYADMIN role has the capability to manage object grants globally, which includes within managed access schemas. Other roles, such as SYSADMIN or database owners, do not inherently have this privilege unless explicitly granted.

 This is the correct answer because it allows the company to ingest data from different regions using a storage integration for the external stage. A storage integration is a feature that enables secure and easy access to files in external cloud storage from Snowflake. A storage integration can be used to create an external stage, which is a named location that references the files in the external storage. An external stage can be used to load data into Snowflake tables using the COPY INTO command, or to unload data from Snowflake tables using the COPY INTO LOCATION command. A storage integration can support multiple regions and cloud platforms, as long as the external storage service is compatible with Snowflake12.

Snowflake Documentation: Storage Integrations

Snowflake Documentation: External Stages

The requirement is for the data to be accessible as quickly as possible after it arrives in the external stage with minimal coding effort.

Option A:Snowpipe with auto-ingest is a service that continuously loads data as it arrives in the stage. With auto-ingest, Snowpipe automatically detects new files as they arrive in a cloud stage and loads the data into the specified Snowflake table with minimal delay and no intervention required. This is an ideal low-maintenance solution for the given scenario where files are arriving at a very high frequency.

Option E:Using a combination of a task and a stream allows for real-time change data capture in Snowflake. A stream records changes (inserts, updates, and deletes) made to a table, and a task can be scheduled to trigger on a very short interval, ensuring that changes are processed into the dashboard tables as they occur.

 According to the Snowflake documentation, when loading data into a table that captures the load time in a column with a default value of either CURRENT_TIME () or CURRENT_TIMESTAMP(), the default value is evaluated once per COPY statement, not once per row. Therefore, all rows loaded using a specific COPY statement will have the same timestamp value. This behavior ensures that the timestamp value reflects the time when the data was loaded into the table, not when the data was read from the source or created in the source. References:

Snowflake Documentation: Loading Data into Tables with Default Values

Snowflake Documentation: COPY INTO table

The Snowflake system functions used to view and monitor the clustering metadata for a table are:

SYSTEM$CLUSTERING_INFORMATION

SYSTEM$CLUSTERING_DEPTH

Comprehensive But Short Explanation:

TheSYSTEM$CLUSTERING_INFORMATIONfunction in Snowflake returns a variety of clustering information for a specified table. This information includes the average clustering depth, total number of micro-partitions, total constant partition count, average overlaps, average depth, and a partition depth histogram. This function allows you to specify either one or multiple columns for which the clustering information is returned, and it returns this data in JSON format.

TheSYSTEM$CLUSTERING_DEPTHfunction computes the average depth of a table based on specified columns or the clustering key defined for the table. A lower average depth indicates that the table is better clustered with respect to the specified columns. This function also allows specifying columns to calculate the depth, and the values need to be enclosed in single quotes.

The AUTO_SUSPEND parameter controls the amount of time, in seconds, of inactivity after which a warehouse is automatically suspended. If the parameter is set to NULL, the warehouse never suspends. If the parameter is set to ‘0’, the warehouse suspends immediately after executing a query. Therefore, the execution behavior of the two virtual warehouses will be different depending on the AUTO_SUSPEND value. The warehouse with NULL value will keep running until it is manually suspended or the resource monitor limits are reached. The warehouse with ‘0’ value will suspend as soon as it finishes a query and release the compute resources. References:

ALTER WAREHOUSE

Parameters

The most performant design for processing changed records, considering the need to both update records in thef_ordersfact table and load changes into theorder_repairstable, is to use one stream and two tasks. The stream will monitor changes in the orders table, capturing both inserts and updates. The first task would apply these changes to thef_ordersfact table, ensuring all dimensions are accurately represented. The second task would use the same stream to insert relevant changes into theorder_repairstable, which is critical for the Accounting department's monthly review. This method ensures efficient processing by minimizing the overhead of managing multiple streams and synchronizing between them, while also allowing specific tasks to optimize for their target operations.

The data formats that are supported for the messages when using the Snowflake Connector for Kafka are Avro and JSON. These are the two formats that the connector can parse and convert into Snowflake table rows. The connector supports both schemaless and schematized JSON, as well as Avro with or without a schema registry1. The other options are incorrect because they are not supported data formats for the messages. CSV, XML, and Parquet are not formats that the connector can parse and convert into Snowflake table rows. If the messages are in these formats, the connector will load them as VARIANT data type and store them as raw strings in the table2. References: Snowflake Connector for Kafka | Snowflake Documentation, Loading Protobuf Data using the Snowflake Connector for Kafka | Snowflake Documentation

Event notifications in Snowpipe are messages sent by cloud storage providers to notify Snowflake of new or modified files in a stage. Snowpipe uses these notifications to trigger data loading from the stage to the target table. When a pipe is paused, event messages received for the pipe enter a limited retention period, which varies depending on the cloud storage provider. If the pipe is not resumed within the retention period, the event messages will be discarded and the data will not be loaded automatically. To load the data, the pipe must be resumed and the COPY command must be executed manually. This is a characteristic of event notifications in Snowpipe that distinguishes them from other options. References: Snowflake Documentation: Using Snowpipe, Snowflake Documentation: Pausing and Resuming a Pipe

 According to the SnowPro Advanced: Architect documents and learning resources, the requirement to allow data sharing between two companies that are not on the same cloud platform is to set up data replication to the region and cloud platform where the consumer resides. Data replication is a feature of Snowflake that enables copying databases across accounts in different regions and cloud platforms. Data replication allows data providers to securely share data with data consumers across different regions and cloud platforms by creating a replica database in the consumer’s account. The replica database is read-only and automatically synchronized with the primary database in the provider’s account. Data replication is useful for scenarios where data sharing is not possible or desirable due to latency, compliance, or security reasons1. The other options are incorrect because they are not required or feasible to allow data sharing between two companies that are not on the same cloud platform. Option A is incorrect because creating a pipeline to write shared data to a cloud storage location in the target cloud provider is not a secure or efficient way of sharing data. It would require additional steps to load the data from the cloud storage to the consumer’s account, and it would not leverage the benefits of Snowflake’s data sharing features. Option B is incorrect because ensuring that all views are persisted is not relevant for data sharing across cloud platforms. Views can be shared across cloud platforms as long as they reference objects in the same database. Persisting views is an option to improve the performance of querying views, but it is notrequired for data sharing2. Option D is incorrect because Company A and Company B do not need to agree to use a single cloud platform. Data sharing is possible across different cloud platforms using data replication or other methods, such as listings or auto-fulfillment3. References: ReplicatingDatabases Across Multiple Accounts | Snowflake Documentation, Persisting Views | Snowflake Documentation, Sharing Data Across Regions and Cloud Platforms | Snowflake Documentation

 A reader account is a type of Snowflake account that allows external users to access data shared by a provider account without being a Snowflake customer. A reader account can be created and managed by the provider account, and can use the Snowflake web interface or JDBC/ODBC drivers to query the shared data. A reader account is billed to the provider account based on the credits consumed by the queries1. A secure view is a type of view that applies row-level security filters to the underlying tables, and masks the data that is not accessible to the user. A secure view can be shared with a reader account to provide granular and governed access to the data2. In this scenario, creating a reader account for the partner and sharing the data sets as secure views would be the most cost-effective and secure design, while using the required Snowflake features, because:

It would avoid the data transfer and storage costs of using an S3 bucket as a destination, and the potential security risks of exposing the data to unauthorized access or modification.

It would avoid the complexity and overhead of publishing the data sets on the Snowflake Marketplace, and the potential loss of control over the data ownership and pricing.

It would avoid the need to create a database user for the partner and grant them access to the required data sets, which would require the partner to have a Snowflake account and consume the provider’s resources.

Reader Accounts

Secure Views

Snowflake stored procedures can execute using either caller’s rights or owner’s rights. When a procedure runs with owner’s rights, it executes using the privileges of the role that owns the procedure—not the role invoking it.

By creating a stored procedure owned by the order_manager role (which has DELETE privileges) and defining it to run with owner’s rights, the Architect can encapsulate the business logic (delete records older than 5 years) securely (Answer C). Granting only USAGE on the procedure to order_admin allows them to execute the cleanup without directly holding DELETE privileges on the table.

Caller’s rights would fail because order_admin lacks DELETE privileges. Snowflake does not support runtime selection of execution rights. This is a classic SnowPro Architect security pattern: least privilege access combined with controlled procedural execution.

The get command in SnowSQL is a convenient way to download files from an internal stage to a local directory. The get command can be used in interactive mode or in a script, and it supports wildcards and parallel downloads. The get command also allows specifying the overwrite option, which determines how to handle existing files with the same name2

The Snowflake Connector for Python, the Snowflake API endpoint, and the get command in Snowsight are not recommended methods for downloading files from an internal stage, because they require more operational overhead than the get command in SnowSQL. The Snowflake Connector for Python and the Snowflake API endpoint require writing and maintaining code to handle the connection, authentication, and file transfer. The get command in Snowsight requires using the web interface and manually selecting the files to download34 References:

1: SnowPro Advanced: Architect | Study Guide

2: Snowflake Documentation | Using the GET Command

3: Snowflake Documentation | Using the Snowflake Connector for Python

4: Snowflake Documentation | Using the Snowflake API

Snowflake Documentation | Using the GET Command in Snowsight

 SnowPro Advanced: Architect | Study Guide

 Using the GET Command

 Using the Snowflake Connector for Python

 Using the Snowflake API

[Using the GET Command in Snowsight]

The image shows a SQL query that can be used to identify which queries are spilled to remote storage and suggests changing the warehouse parameters to address this issue. Spilling to remote storage occurs when the memory allocated to a warehouse is insufficient to process a query, and Snowflake uses disk or cloud storage as a temporary cache. This can significantly slow down the query performance and increase the cost. To troubleshoot this issue, a Snowflake Architect can run the query shown in the image to find out which queries are spilling, how much data they are spilling, and which warehouses they are using. Then, the architect can adjust the warehouse size, type, or scaling policy to provide enough memory for the queries and avoid spilling12. References:

Recognizing Disk Spilling

Managing the Kafka Connector

For the configuration of data unloading in Snowflake, the valid option among the provided choices is "C." This is because Snowflake supports unloading data into Google Cloud Storage using the COPY INTO <location> command with specific configurations. The configurations listed in option C, such as Parquet file format with UTF-8 encoding and gzip compression, are all supported by Snowflake. Notably, Parquet is a columnar storage file format, which is optimal for high-performance data processing tasks in Snowflake. The UTF-8 file encoding and gzip compression are both standard and widely used settings that are compatible with Snowflake’s capabilities for data unloading to cloud storage platforms.

 A star schema model is a type of dimensional data model that consists of a single fact table and multiple dimension tables. A 3NF model is a type of relational data model that follows the third normal form, which eliminates data redundancy and ensures referential integrity. A Snowflake Architect might use a star schema model rather than a 3NF model when designing a data architecture to run in Snowflake for the following reasons:

A star schema model is more suitable for analytical queries that require aggregating and slicing data across different dimensions, such as those performed by a BI tool. A 3NF model is more suitable for transactional queries that require inserting, updating, and deleting individual records.

A star schema model is simpler and faster to query than a 3NF model, as it involves fewer joins and less complex SQL statements. A 3NF model is more complex and slower to query, as it involves more joins and more complex SQL statements.

A star schema model can provide a simple flattened single view of the data to a particular group of end users, such as business analysts or data scientists, who need to explore and visualize the data. A 3NF model can provide a more detailed and normalized view of the data to a different group of end users, such as application developers or data engineers, who need to maintain and update the data.

The other options are not valid reasons for choosing a star schema model over a 3NF model in Snowflake:

Snowflake can handle the joins implied in a 3NF data model, as it supports ANSI SQL and has a powerful query engine that can optimize and execute complex queries efficiently.

The Architect can use both star schema and 3NF models to remove data duplication from the data stored in Snowflake, as both models can enforce data integrity and avoid data anomalies. However, the trade-off is that a star schema model may have more data redundancy than a 3NF model, as it denormalizes the data for faster query performance, while a 3NF model may have less data redundancy than a star schema model, as it normalizes the data for easier data maintenance.

The Architect can use both star schema and 3NF models to design a landing zone to receive raw data into Snowflake, as both models can accommodate different types of data sources and formats. However, the choice of the model may depend on the purpose and scope of the landing zone, such as whether it is a temporary or permanent storage, whether it is a staging area or a data lake, and whether it is a single source or a multi-source integration.

Snowflake Architect Training

Data Modeling: Understanding the Star and Snowflake Schemas

Data Vault vs Star Schema vs Third Normal Form: Which Data Model to Use?

Star Schema vs Snowflake Schema: 5 Key Differences

Dimensional Data Modeling - Snowflake schema

Star schema vs Snowflake Schema

A pipe is a Snowflake object that continuously loads data from files in a stage (internal or external) into a table. A pipe can be configured to use auto-ingest, which means that Snowflake automatically detects new or modified files in the stage and loads them into the table without any manual intervention1.

A pipe is the most cost-effective way to bring large numbers of small JSON files into a Snowflake table, because it minimizes the number of COPY commands executed and the number of micro-partitions created. A pipe can use file aggregation, which means that it can combine multiple small files into a single larger file before loading them into the table. This reduces the load time and the storage cost of the data2.

An external table is a Snowflake object that references data files stored in an external location, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. An external table does not store the data in Snowflake, but only provides a view of the data for querying. An external table is not a cost-effective way to bring data into a Snowflake table, because it does not support file aggregation, and it requires additional network bandwidth and compute resources to query the external data3.

A stream is a Snowflake object that records the history of changes (inserts, updates, and deletes) made to a table. A stream can be used to consume the changes from a table and apply them to another table or a task. A stream is not a way to bring data into a Snowflake table, but a way to process the data after it is loaded into a table4.

A copy command is a Snowflake command that loads data from files in a stage into a table. A copy command can be executed manually or scheduled using a task. A copy command is not a cost-effective way to bring large numbers of small JSON files into a Snowflake table, because it does not support file aggregation, and it may create many micro-partitions that increase the storage cost of the data5.

 Pipes : Loading Data Using Snowpipe : External Tables : Streams : COPY INTO <title>

To improve the performance of queries on semi-structured data, such as JSON stored in a VARIANT column, Snowflake’s search optimization service can be utilized. By adding search optimization specifically for the longitude and latitude fields within the VARIANT column, the system can perform point lookups and substring queries more efficiently. This will allow for faster retrieval of weather statistics, which is critical for the drivers to receive timely updates.

Comprehensive and Detailed 150 to 250 words of Explanation From Snowflake SnowPro Architect exam scope and all publicly documented material:

Clustering keys are most beneficial when they improve micro-partition pruning for common filter patterns and when the chosen columns provide a useful ordering that co-locates data. A common heuristic is to place lower-cardinality columns earlier (to quickly narrow partitions) and then add a higher-cardinality column that further reduces scanned partitions for selective access paths. Here, C_DATE has very low NDV (110), making it an excellent leading key to organize data by date and enable strong pruning for time-bound queries typical of event tables. Next, A_ID (11K) is moderate cardinality and can further segment data within a date range, helping point lookups or narrow scans by identifier. For the third key, the options force choosing between very high-cardinality event activity columns; selecting EVENT_ACT_0 (1.1G) is preferable to EVENT_ACT_4 (2.2G) because it is comparatively less distinct while still supporting additional pruning when queries filter by that attribute. This ordering aligns with Snowflake guidance: keep keys few, ordered to match common predicates, and avoid excessively high-cardinality keys unless they directly match frequent selective filters.

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QUESTION NO: 2 [Performance Optimization and Monitoring]

An Architect has implemented the search optimization service for a table. A user adds a new column to the table and there is a decrease in query performance. The Architect executes the DESCRIBE SEARCH OPTIMIZATION command and finds that the newly added column was not included in the search access path. Why did this occur?

A. The new column has a data type that is not supported by the search optimization service.

B. The new column is automatically included in the search access path, but there is a time delay before it takes effect.

C. The ON clause was used when enabling the search optimization service, which does not automatically include new columns.

D. The search optimization property needs to be dropped and then added back for the changes to take effect.

Answer: C

Comprehensive and Detailed 150 to 250 words of Explanation From Snowflake SnowPro Architect exam scope and all publicly documented material:

Search Optimization Service (SOS) builds and maintains a “search access path” for predicates on specific columns. When SOS is enabled without specifying columns, Snowflake can manage broader coverage depending on configuration; however, if SOS is enabled using an ON clause (targeting particular columns/expressions), the access path is explicitly defined and does not automatically expand to include newly added columns. That means queries filtering on the new column won’t benefit from SOS, and if workload patterns shift toward that column, perceived performance can drop relative to expectations. DESCRIBE SEARCH OPTIMIZATION exposing that the new column is absent from the access path is consistent with SOS being configured for a fixed set of columns. In SnowPro Architect terms, this is an operational/design consideration: SOS requires deliberate selection of access paths aligned to query predicates, and schema evolution (adding columns) may require revisiting SOS configuration to include new predicates that matter. This also reinforces the cost/performance tradeoff: SOS accelerates selective point-lookups and highly selective filters, but it must be targeted and maintained as query patterns and schemas change.

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QUESTION NO: 3 [Architecting Snowflake Solutions]

An Architect is defining transaction rules to adhere to ACID properties to ensure that executed statements are either committed or rolled back. Based on this scenario, what characteristics of transactions should be considered? (Select TWO).

A. The autocommit setting can be changed inside a stored procedure.

B. Explicit transactions should contain DDL, DML, and query statements.

C. Explicit transactions should contain only DML statements and query statements. All DDL statements implicitly commit active transactions.

D. An explicit transaction can be started by executing a BEGIN WORK statement and can be ended by executing a COMMIT WORK statement.

E. An explicit transaction can be started by executing a BEGIN TRANSACTION statement and can be ended by executing an END TRANSACTION statement.

Answer: C, D

Comprehensive and Detailed 150 to 250 words of Explanation From Snowflake SnowPro Architect exam scope and all publicly documented material:

Snowflake supports transactional behavior for DML (and SELECT statements within a transaction context), but DDL statements have special behavior: many DDL operations implicitly commit the current transaction. Because of that, mixing DDL with DML inside an explicit transaction undermines the “all-or-nothing” rollback expectations and complicates ACID-driven rules. Therefore, a key design characteristic is that explicit transactions should generally be limited to DML and query statements, while recognizing that DDL can implicitly commit and break transactional boundaries (Choice C). Additionally, Snowflake supports explicit transaction control statements using standard SQL forms; BEGIN WORK and COMMIT WORK are valid ways to open and close an explicit transaction (Choice D). From an architecting standpoint, this matters for pipeline design, error handling, and idempotency: multi-step loads/merges should group logically atomic DML steps into an explicit transaction so they commit together or roll back together, but DDL (like CREATE/ALTER) should be separated or executed with full awareness of implicit commits. This is consistent with SnowPro Architect expectations around reliable data engineering patterns and correctness under failure.

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QUESTION NO: 4 [Snowflake Data Engineering]

A Snowflake account has the following parameters:

MIN_DATA_RETENTION_TIME_IN_DAYS is set to 5 at the account level.

DATA_RETENTION_TIME_IN_DAYS is set to 4 on database DB1, and 6 on Schema1 in DB1.

DATA_RETENTION_TIME_IN_DAYS is set to 5 on database DB2, and 8 on Schema2 in DB2.What will be the result?

A. DB1 and Schema1 retained 5 days; DB2 and Schema2 retained 5 days.

B. DB1 and Schema1 retained 4 days; DB2 and Schema2 retained 5 days.

C. DB1 retained 4 days and Schema1 6 days; DB2 retained 5 days and Schema2 8 days.

D. DB1 retained 5 days and Schema1 6 days; DB2 retained 5 days and Schema2 8 days.

Answer: D

Comprehensive and Detailed 150 to 250 words of Explanation From Snowflake SnowPro Architect exam scope and all publicly documented material:

Snowflake Time Travel retention is governed by a parameter hierarchy and by edition-dependent limits, but the key behavior in this scenario is the minimum retention guardrail. MIN_DATA_RETENTION_TIME_IN_DAYS sets a floor: objects cannot effectively use a DATA_RETENTION_TIME_IN_DAYS value below that minimum. With an account-level minimum of 5 days, a database-level setting of 4 days is below the permitted minimum, so the effective retention for DB1 cannot be 4 days; it must be at least 5 days. Meanwhile, Schema1 is set to 6 days, which is above the minimum and should apply at the schema level (schemas can override their parent database settings within allowed bounds). For DB2, the database setting is 5 days, which matches the minimum and is valid; Schema2 at 8 days is also valid and applies to objects under that schema where applicable. From an architectural and operational perspective, this highlights two exam-relevant points: (1) retention is controlled via hierarchical parameters (account → database → schema → table), and (2) minimum retention settings enforce governance constraints across environments, preventing overly aggressive reductions that might violate recovery requirements.

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QUESTION NO: 5 [Performance Optimization and Monitoring]

An Architect has configured the search optimization service on a table, but metrics show that performance of a number of regularly executed queries is not improving. What could be causing this? (Select TWO).

A. The queries contain SEARCH functions.

B. The queries exceed the predicate limit.

C. The queries contain a predicate mismatch.

D. The queries are running against tables that contain semi-structured data.

E. The queries have a scalar subquery that queries the same table as the table in an outer query.

Answer: B, C

Comprehensive and Detailed 150 to 250 words of Explanation From Snowflake SnowPro Architect exam scope and all publicly documented material:

Search Optimization Service improves performance by maintaining additional search structures to accelerate selective predicates for supported query patterns. Two common reasons it may not help are (1) exceeding the predicate limit and (2) predicate mismatch. If queries include more predicates than SOS can leverage effectively (or beyond documented limits for the access path), Snowflake may not apply the optimization and will revert to normal micro-partition pruning and scanning behavior (Choice B). Predicate mismatch occurs when the query’s filter conditions do not align with the SOS access path definition—examples include using different columns than those optimized, applying functions/casts that prevent using the access path, or using non-supported predicate forms—so SOS cannot accelerate those queries (Choice C). Semi-structured data alone does not inherently prevent SOS benefits; SOS can be used with certain patterns, including on some expressions, but it still must match the configured access path. Likewise, the presence of SEARCH functions is not the core issue; SOS is about access paths and supported predicates, not requiring SEARCH() usage. Architecturally, this is why SOS should be selectively enabled for columns and predicate shapes that dominate high-cost workloads, and monitored/adjusted as query patterns evolve.

 The warehouse MY_WH will only be active when there are results in the stream. This is because the task is created based on a stream, which means that the task will only be executed when there are new data in the stream. Additionally, the warehouse is set to auto_suspend - 60, which means that the warehouse will automatically suspend after 60 seconds of inactivity. Therefore, the warehouse will only be active when there are results in the stream. References:

[CREATE TASK | Snowflake Documentation]

[Using Streams and Tasks | Snowflake Documentation]

[CREATE WAREHOUSE | Snowflake Documentation]

According to the Snowflake documentation, tasks are objects that enable scheduling and execution of SQL statements or JavaScript user-defined functions (UDFs) in Snowflake. Tasks can be used to automate data loading, transformation, and maintenance operations. Snowflake scripting is a feature that allows writing procedural logic using SQL statements and JavaScript UDFs. Snowflake scripting can be used to create complex workflows and orchestrate tasks. Therefore, the best option to automate the daily import of two files from an external stage into Snowflake, join and aggregate the data, and produce a final result set is to create a task using Snowflake scripting that will import the files using the COPY INTO command, and then call a UDF to perform the join and aggregation logic. The UDF can return a table or a variant value as the final result set. References:

Tasks

Snowflake Scripting

User-Defined Functions