Data-Architect Salesforce Certified Platform Data Architect Plat-Arch-201 Questions and Answers

A performance test plan is required for any Force.com application that has highly customized code or large volumes of data.  You need to create and submit a performance test plan to Salesforce customer support before conducting any performance testing in your sandbox or production org 2 .  Applications with highly customized code or large volumes of data should be performance tested to ensure they meet the expected response time and throughput

Changing the opportunity amount field access to read only for sales users using field level security is the best way to address the overriding issue. This way, the sales users can still view the opportunity amount field but cannot edit it, and PPS can update it as the trusted source

Data skew occurs when a large number of child records are associated with a single parent record, or when a single user owns a large number of records. This can cause performance issues and lock contention.  To avoid data skew, the data architect should limit assigning one user 10,000 records ownership, limit associating 10,000 opportunities to one account, and limit associating 10,000 records looking up to the same record

According to the Salesforce documentation, a relationship field is a field that allows linking one object to another object in Salesforce. There are different types of relationship fields that have different characteristics and behaviors, such as master-detail, lookup, indirect lookup, external lookup, etc.

To recommend a type of relationship field for this scenario, where legacy inventory data will be stored in a custom child object called Inventory__c, inventory data should be related to the standard Account object, the Inventory__c object should inherit the same sharing rules as the Account object, and anytime an Account record is deleted in Salesforce, the related Inventory__c record(s) should be deleted as well, a data architect should recommend:

Master-detail relationship field on Inventory__c, related to Account (option B). This means creating a field on the Inventory__c object that references the Account object as its parent. A master-detail relationship field establishes a parent-child relationship between two objects, where the parent object controls certain behaviors of the child object. For example, a master-detail relationship field can:

Inherit the sharing and security settings from the parent object to the child object. This means that the users who can access and edit the parent record can also access and edit the related child records.

Cascade delete from the parent object to the child object. This means that when a parent record is deleted, all the related child records are also deleted.

Roll up summary fields from the child object to the parent object. This means that the parent object can display aggregated information from the child records, such as count, sum, min, max, or average.

Master-detail relationship field on Account, related to Inventory__c (option A) is not a good solution, as it reverses the direction of the relationship. This means creating a field on the Account object that references the Inventory__c object as its parent. This is not possible, as a standard object cannot be on the detail side of a master-detail relationship. Indirect lookup relationship field on Account, related to Inventory__c (option C) is also not a good solution, as it is a special type of relationship field that allows linking a custom object to a standard object on an external system using an indirect reference. This is not applicable for this scenario, as both objects are in Salesforce and do not need an external reference. Lookup relationship field on Inventory__c related to Account (option D) is also not a good solution, as it establishes a looser relationship between two objects than a master-detail relationship. A lookup relationship field does not inherit sharing and security settings from the parent object to the child object, does not cascade delete from the parent object to the child object, and does not roll up summary fields from the child object to the parent object.

Both A and B are automated approaches to purge detailed transactional records from Salesforce and aggregate them at a summary level 1 . You can use a third-party integration tool (ETL) or schedulable batch Apex to perform these tasks. Option C is not correct because a third-party business intelligence system does not purge data from Salesforce, but only analyzes it. Option D is not correct because Apex triggers are not automated, but execute when a record is inserted, updated, deleted, or undeleted.

Option B is correct because data model is a key artifact that an architect should use to document the data architecture for a multi-system, enterprise Salesforce implementation 1 .  Data model describes the structure and relationship of data entities within an organization 2 .  Option C is correct because integration specification is another key artifact that an architect should use to document the data architecture for a multi-system, enterprise Salesforce implementation 1 .  Integration specification defines the scope, requirements, design, testing, and deployment of integration solutions between Salesforce and other systems 3 .  Option A is not correct because user stories are not key artifacts for documenting the data architecture, but agile development tools that capture the features and functionalities that users want from a system 4 . Option D is not correct because non-functional requirements are not key artifacts for documenting the data architecture, but quality attributes that specify how well a system performs its functions.

The best way to implement the storage of master key within Salesforce is to store it on the contact object as an external ID field for referential imports. This way, the data architect can use the master key as a unique identifier to match records from different systems and avoid duplicates. The other options are not feasible because they either require additional storage or do not support referential imports.

According to the Salesforce documentation, SOQL is a query language that allows querying data from Salesforce objects and fields. SOQL queries have various clauses and operators that can be used to filter and sort the results. However, some clauses and operators can affect the performance of SOQL queries by increasing the cost or complexity of executing them.

To make this query more performant, a data architect should change the where clause to filter by a deterministic defined value (option D). This means using a filter condition that specifies a concrete value or range of values for a field, such as WhatId = ‘001xx000003DGg3’ or WhatId IN (‘001xx000003DGg3’, ‘001xx000003DGg4’). This can improve the performance of the query by reducing the number of records that need to be scanned and returned. A deterministic defined value can also leverage an index on the field, which can speed up the query execution.

Removing description from the requested field set (option A) is not a good solution, as it can affect the functionality or usability of the query. The description field may contain important or relevant information that is needed for the process. Changing the query to SOSL (option B) is also not a good solution, as SOSL is a different query language that allows searching text fields across multiple objects. SOSL queries have different syntax and limitations than SOQL queries, and may not return the same results or performance. Adding limit 100 to the query (option C) is also not a good solution, as it can affect the completeness or accuracy of the query. The limit clause specifies the maximum number of records that can be returned by the query, which may not include all the records that match the filter condition.

Creating an external object Invoice_x with a Lookup relationship with Opportunity is the best option for modeling invoices, considering performance and data storage space. An external object allows the data to be stored in the ERP system and accessed via OData in Salesforce. This reduces the data storage consumption in Salesforce and improves the performance of queries and reports. A Lookup relationship allows the sales reps to view the invoice status and amount from the opportunity page.  The other options would either consume more data storage space, require additional customization, or not provide real-time data access

The data architect should recommend using AppExchange package for backup and restore. AppExchange is a marketplace for Salesforce apps and solutions that can be installed and configured in Salesforce orgs. There are several AppExchange packages that provide backup and restore functionality for Salesforce data, such as OwnBackup, Odaseva, or Spanning. These packages can perform daily backups of Salesforce data to a secure cloud storage, and provide restore capability to avoid any data loss in case of outage. Option B is incorrect because using ETL (Extract, Transform, Load) for backup and restore from EDW (Enterprise Data Warehouse) will require additional development effort and may not be reliable or secure. Option C is incorrect because using Bulk API to extract data on daily basis to EDW and REST API for restore will require additional integration effort and may not be scalable or performant. Option D is incorrect because changing weekly backup process to daily backup, and implementing a custom restore solution will require additional configuration effort and may not be robust or compliant.

 Skinny tables are custom tables that contain frequently used fields from a standard or custom object. They are used to improve performance by reducing the number of database joins required for queries.  Skinny tables have the following characteristics 1 :

They do not include soft-deleted records, which means they only contain active records and save space.

They avoid resource intensive joins by storing data from multiple objects in one table, which reduces the query time and complexity.

Their tables are kept in sync with their source tables when the source tables are modified, which ensures data consistency and accuracy.

To provide the sales team with the required functionality, a data architect should use a mashup page that will present the billing system records within Salesforce. A mashup page is a web page that combines data from multiple sources into a single integrated view. A mashup page can be created using Visualforce or Lightning Web Components, and can use Salesforce Connect or custom integrations to access external data from the on-premise web-based billing and invoice system. This will allow the sales team to pull a customer record and view their account status, invoice history, and opportunities without navigating outside of Salesforce. Option A is incorrect because creating a custom object and migrating the last 12 months of invoice data into Salesforce so it can be displayed on the account layout will consume a lot of storage space and may not reflect the latest data from the billing system. Option B is incorrect because writing an Apex callout and populating a related list to display on the account record will require additional development effort and may not be scalable or performant for large volumes of data. Option D is incorrect because creating a visual force tab with the billing system encapsulated within an iframe will not allow the sales team to view the billing system records within the customer record, but rather in a separate tab.

The three characteristics of a Skinny table that help improve report and query performance are: Skinny tables can contain frequently used fields and thereby help avoid joins. Skinny tables are kept in sync with changes to data in the source tables. Skinny tables do not include records that are available in the recycle bin. These characteristics are beneficial because they reduce the query complexity and execution time, and improve the data accuracy and freshness.  For example, skinny tables can contain frequently used fields from multiple objects, such as Account and Contact, and thereby help avoid joins that can slow down queries 4 .  Skinny tables are updated automatically when the source tables are modified, so they always reflect the latest data 5 . Skinny tables do not include records that are available in the recycle bin, so they only contain active records that are relevant for reports and queries.

The best license to provide these features to the franchisees is the Partner Community license. Partner Community licenses are designed for external users who collaborate with your sales team on deals, such as resellers, distributors, or brokers. Partner Community users can access standard CRM objects, such as accounts, contacts, leads, opportunities, campaigns, and reports.  They can also access custom objects and run dashboards 1 2 . Salesforce Sales Cloud license is not a good option because it is intended for internal users who need full access to standard CRM and custom apps. Lightning Platform license is not a good option because it is intended for users who need access to custom apps but not to standard CRM functionality.  Customer Community license is not a good option because it is intended for external users who need access to customer support features, such as cases and knowledge articles, but not to sales features

The best solution to resolve the error of “Unable to lock a row” during the account load is to perform batch job in serial mode and reduce batch size. This is because roll-up summary fields are calculated synchronously when the parent record is updated, and asynchronously when the child record is updated. Therefore, updating many child records at once can cause locking issues on the parent record.  To avoid this, it is recommended to use serial mode and smaller batch sizes when loading data using tools like Data Loader or Bulk API 1 2 . Leverage data loader platform API to load data is not a good option because it does not specify the mode or batch size. Perform batch job in parallel mode and reduce batch size is not a good option because parallel mode can still cause locking issues even with smaller batches.  Defer roll-up summary fields calculation during data migration is not a good option because it is not possible to defer or disable roll-up summary fields calculation

Importing Account records first followed by order records after sorting orders by AccountID is the recommended way to avoid locking issues during import. This can reduce the number of lock contention errors by minimizing the number of parent records that are concurrently processed by multiple batches.  Sorting orders by AccountID can also group the child records by their parent records and avoid updating the same parent record in different batches 3 . Importing Account records first followed by order records after sorting order by OrderID will not help with avoiding locking issues because it does not group the child records by their parent records. Changing the relationship to Lookup and updating the relationship to master-detail after import will not work because changing a relationship from Lookup to master-detail requires that all child records have a parent record, which may not be the case after import. Importing Order records and Account records separately and populating AccountID in orders using batch Apex will not help with avoiding locking issues because it still requires updating the parent records in batches.

Using an AppExchange package for Data Quality is a good way to match Salesforce data against a trusted source, such as an MDM system.  You can use tools like Cloudingo, DupeCatcher, or DemandTools to identify and merge duplicate records, standardize data formats, and enrich data with external sources

Exploring external data sources in Salesforce to build 360 view of customer is the best strategy to satisfy the requirement, as it allows real-time access to data from other systems without storing it in Salesforce 3 . Using a customer data mart may not provide real-time information or may require additional integration efforts. Migrating customer activities from all 4 systems into Salesforce may exceed the storage limits or cause data quality issues. Periodically uploading summary information in Salesforce may not provide current or detailed information.

The three activities that will be required to identify a successful master data management strategy are:

Identify data to be replicated: This activity involves determining which data elements need to be copied from one system to another, and how frequently the replication should occur. This can help ensure data consistency and availability across systems.

Create a data archive strategy: This activity involves defining how historical data will be stored, accessed, and deleted over time. This can help optimize data storage, performance, and compliance.

Define the systems of record for critical data: This activity involves identifying which system owns and maintains the authoritative version of each data element.  This can help avoid data conflicts and duplication across systems 6 7 .

Install a data warehouse is not a required activity, but rather a possible option for consolidating data from multiple sources for analytics purposes. Choose a Business Intelligence tool is not a required activity, but rather a possible option for visualizing and reporting on data from various sources.

Integrating with a third-party database or service for address validation and enrichment is a scalable strategy that can improve the quality of the Account addresses by comparing them with a reliable source of data 1 .  Leveraging Data.com Clean to clean up Account address fields with the D & B database is another scalable strategy that can automatically update and enrich Account records with verified information from Data.com 2 .

The best solution to make sure decrypted values are exported during data export is to create another field to copy data from encrypted field and use this field in export. This is because classic encryption does not support exporting decrypted values of encrypted fields. The view encrypted data permission only allows users to view decrypted values in the user interface, but not in reports or data exports. Therefore, a workaround is to create a formula field or a workflow field update that copies the value of the encrypted field to another field, and use that field for data export. However, this solution has some drawbacks, such as exposing sensitive data in plain text and consuming extra storage space.  A better solution would be to use Shield Platform Encryption, which supports exporting decrypted values of encrypted fields with the Export Encrypted Data permission

According to  this article , person accounts are designed to store information about individual people by combining certain account and contact fields into a single record. This is suitable for a large B2C retailer that needs to model the consumer account structure in Salesforce.

 Implementing a custom big object to store the data is the best recommendation to reduce the performance degradation of the platform, as it allows storing large volumes of data that do not need real-time access or reporting. Custom big objects can be defined using metadata API or developer console, and support up to 1 billion records per object. Creating a custom object or using the standard order object would consume a lot of storage space and impact the performance of queries and reports. Using a standard big object defined by salesforce would not be applicable for order records, as standard big objects are predefined for specific use cases such as audit trails or field history.

Data Loader uses external Id fields to match records in the CSV file with records in Salesforce during an upsert operation. If the CSV file contains duplicate external Id values within the same file, Data Loader will throw an error saying “Duplicate Id Specified” and will not process those records. Therefore, it is important to ensure that the CSV file does not have any duplicate external Id values before using Data Loader to upsert records.

 Creating validation rules to check if the required attributes are entered is the best option to mandate this when customers are created in Salesforce. Validation rules allow you to specify criteria that must be met before a record can be saved. You can use validation rules to ensure that customers have a first name, last name, and email when they are created in Salesforce. This way, you can prevent incomplete or invalid data from being sent to your MDM solution.

 According to the Salesforce documentation 1 , ownership skew occurs when a large number of records (more than 10,000) are owned by a single user or queue. This can cause performance issues and lock contention when multiple users try to access or update those records. To prevent ownership skew, some of the recommended practices are:

Assign a default user as the owner of the records and do not assign any role to the default user (option C). This way, the records will not be visible to other users in the role hierarchy and will not cause sharing recalculations.

Keep users out of public groups that can be used as the source for sharing rules (option B). Sharing rules based on public groups can cause excessive sharing calculations and lock contention when many records are owned by a single user or queue.

Assign a default user as the owner of the records and assign the top most role in the hierarchy to the default user (option E). This way, the records will be visible to all users in the role hierarchy, but will not cause sharing recalculations or lock contention.

Assigning a default user as the owner of the records and assigning a role in the hierarchy (option A) is not a good practice, as it can cause sharing recalculations and lock contention when the role is updated or moved. Assigning “view all” permission on profile to give access to the records (option D) is also not a good practice, as it can bypass the security and sharing model and expose sensitive data to unauthorized users.

A master data management (MDM) strategy for customer data can help UC ensure that all systems have access to the same customer information, without loading or duplicating data in all systems. An MDM strategy can also help UC avoid data conflicts and inconsistencies that may arise from having multiple systems as owners of data.

Implementing duplicate rules (option D) is the best solution to remediate the duplication issue, as it allows the data architect to identify and merge duplicate accounts in Salesforce using native features and tools. Developing an ETL process that utilizes the merge API to merge the duplicate records (option A) is not a good solution, as it may require more coding and testing effort, and it does not prevent duplicates from being created in Salesforce. Utilizing a data warehouse as the system of truth (option B) is also not a good solution, as it may introduce additional complexity and cost, and it does not address the duplication issue in Salesforce. Extracting the data using data loader and using excel to merge the duplicate records (option C) is also not a good solution, as it may be time-consuming and error-prone, and it does not prevent duplicates from being created in Salesforce.

The best API for retrieving Salesforce org metadata programmatically is the Metadata API. The Metadata API provides access to the metadata that defines the structure and configuration of an org, such as custom objects, fields, workflows, security settings, etc. It also supports deploying, retrieving, creating, updating, and deleting metadata components. The Metadata API can be used with various tools, such as Ant, Workbench, or IDEs.

To build a custom big object to support storing more than 100 million records in Salesforce, a data architect should use Metadata API or Big Object in setup. Metadata API is an API that allows developers to create, retrieve, update, or delete metadata components in Salesforce programmatically. Big Object in setup is a user interface that allows admins to create big objects declaratively without writing code. Both tools can be used to define custom big objects and their fields, indexes, and relationships in Salesforce. Option A is incorrect because DX (Developer Experience) is a set of tools that allows developers to create and manage applications on Salesforce Platform, but it does not support creating big objects directly. Option D is incorrect because Object Manager in setup is a user interface that allows admins to create and manage standard and custom objects in Salesforce, but it does not support creating big objects declaratively.

Key stakeholders from both systems should collaborate with the Architect to determine which system is the system of record on a field level is what the Architect should do to achieve this goal of integrating two different systems with customer records into the Salesforce Account object using Master Data Management. The system of record is the authoritative source of truth for a given entity or field in a given context. Different systems may have different levels of accuracy, completeness, timeliness, or relevance for different fields. Therefore, it is important to involve key stakeholders from both systems who have knowledge and expertise about their data quality and business needs to decide which system should be the system of record for each field. The Architect should facilitate this collaboration and document the decisions and rationale for each field. The other options are not correct or feasible, as they would either delegate or abdicate the responsibility of determining the system of record, ignore or disregard the input from key stakeholders, or assume or impose a default system of record without considering the data quality and business needs.

 A Sales KPI Dashboard is one reporting option that Get Cloudy Consulting should consider to understand key relevant performance figures and help managers take corrective actions where appropriate. A Sales KPI Dashboard can display various metrics that indicate the health and effectiveness of the sales team, such as quota attainment, pipeline value, win rate, average deal size, sales cycle length, and more. A Sales KPI Dashboard can also help identify trends, patterns, and areas for improvement.

Analytics/BI Owners and Data Domain Stewards are appropriate roles as members of a team that makes decisions on enterprise-wide data governance. Analytics/BI Owners are responsible for defining the business requirements and metrics for data analysis and reporting, and Data Domain Stewards are responsible for defining and enforcing the data quality standards and rules for specific data domains. Salesforce Administrators and Operational Data Users are not suitable roles for this team, as they are more focused on the operational aspects of data management, such as configuration, maintenance, and usage.

Grouping batches by the AccountId field can improve the performance and avoid locking issues when loading Opportunity records using the Bulk API in parallel mode 1 .  This is because the AccountId field is indexed and can be used to distribute the records evenly across batches

The recommended solution to ensure long-term performance for the customer categorization process is to implement a new picklist custom field in the Account object with Gold, Silver, and Bronze values. A picklist field is a simple and efficient way to store a predefined set of values for a record. A picklist field has the following benefits:

It allows you to easily filter, sort, and group records by their category values, which can help you analyze and segment your customers.

It does not require creating any additional objects or relationships, which can reduce the data model complexity and maintenance overhead.

It supports large data volumes and can handle millions of records without affecting performance or scalability.

When denormalizing a data model into a single object with a picklist field, the data architect should consider the Bulk API limitations on picklist fields and the standard list view in-line editing.  The Bulk API has a limit of 1,000 distinct picklist values per file 1 , which could be an issue if there are more than 1,000 venues in the future.  The standard list view in-line editing allows users to edit multiple records at once, which could introduce data quality issues if the venue picklist is not validated or restricted 2 . The other options are not relevant to denormalizing a data model.

A required Lookup relationship ensures that the Shipment record must have a value for each of the three parent records, and also prevents the deletion of those parent records if they are referenced by a Shipment record.  A Master-Detail relationship would not allow separate sharing models for each custom object, and a VLOOKUP formula field would not enforce the relationship or prevent deletion

The best design pattern to satisfy the requirement of accessing order data from an on-premise database with REST API access without consuming SF storage limits is to use SF Connect to virtualize the data in SF and avoid storage limits. SF Connect is an integration tool that allows users to access and integrate data from external sources using external objects. External objects are similar to custom objects, except that the data resides in another system and is accessed in real time via web service callouts.  SF Connect supports various adapters to connect to different types of external data sources, such as OData, cross-org, or Apex custom adapter 11 . Migrate and persist the data in SF to take advantage of native functionality is not a good option because it would consume SF storage limits and require data synchronization between systems. Develop a bidirectional integration between the on-premise system and Salesforce is not a good option because it would be complex and costly to implement and maintain, and it would also consume SF storage limits. Build a UI for the on-premise system and iframe it in Salesforce is not a good option because it would not provide a seamless user experience and it would not allow users to search, report, or perform actions on the external data.

 The Data Architect should consider the limitations on Org data storage and the Bulk API limitations on picklist fields when denormalizing the data model into a single Transaction object with a Branch Office picklist.  The Org data storage limit is the total amount of data that can be stored in a Salesforce Org, and it depends on the edition and license type of the Org 1 .  The Bulk API limit on picklist fields is the maximum number of values that can be imported or exported using the Bulk API, and it is 1,000 values per picklist field 2 . These limitations could affect the performance and scalability of the data model, and the Data Architect should plan accordingly.

Using scheduled batch Apex to copy aggregate information into a custom object and delete the original records can improve the performance and reduce the data storage limits by removing unnecessary data and keeping only the summary data that is needed for reporting.  Configuring the Salesforce Archiving feature to archive older records and remove them from the data storage limits can also help with performance and storage issues by moving historical data to a separate system that is still accessible but does not affect the operational data

According to the  Using Mashups  article on Salesforce Developers, one of the techniques for deploying large data volumes is to use mashups to display historical records in Salesforce. The article states that “Mashups are a way to display data from an external system within a Salesforce page without copying or synchronizing the data. Mashups use a combination of Visualforce, Apex callouts, and JavaScript code that runs in the browser. Mashups are useful when you want to display large amounts of read-only data that is stored outside of Salesforce.” Therefore, a data architect should recommend this solution to meet the business requirement of UC.

Having each system pull the record changes from Salesforce using change data capture (option D) is the correct solution for ensuring all systems using customer data are kept up to date, as it allows the systems to subscribe to real-time events from Salesforce and receive notifications when customer records are created, updated, deleted, or undeleted. Sending customer data nightly to the system of truth in a scheduled batch job (option A) or sending customer record changes from Salesforce to each system in a nightly batch job (option B) are not good solutions, as they may cause data latency and inconsistency, and they do not provide real-time updates. Sending customer record changes from Salesforce to the system of truth in real time (option C) is also not a good solution, as it does not address how the other systems will receive the updates from the system of truth.

Changing Opportunity Amount field access to Read Only for sales users field-level security (option C) is the best way to address the overwriting issue, as it prevents sales users from updating the field that is controlled by PPS, and ensures data consistency and accuracy. Creating a custom field for Opportunity amount that PSS updates separating the field sales user updates (option A) or creating a custom field for Opportunity amount that sales users update separating the field that PPS updates (option D) are not good solutions, as they may create confusion and inconsistency with the Opportunity Forecast feature. Changing PSS integration to update only Opportunity Amount field when the value is null (option B) is also not a good solution, as it may cause data loss or conflicts with the sales users’ inputs.

According to the  What is Master Data Management (MDM)?  article, some of the data challenges that an MDM tool can solve are data duplication, data accuracy and quality, and data standardization. The article states that “MDM solutions comprise a broad range of data cleansing, transformation, and integration practices. As data sources are added to the system, MDM initiates processes to identify, collect, transform, and repair data. Once the data meets the quality thresholds, schemas and taxonomies are created to help maintain a high-quality master reference.” Therefore, an MDM tool can help NTO eliminate data duplication across different systems, improve data accuracy and quality by removing errors and inconsistencies, and standardize data formats and definitions for better integration and analysis.

The two solutions that a data architect should recommend to preserve the date timestamps and ownership on records being migrated are:

Enable update records with Inactive Owners Permission: This permission allows users to update record owner and sharing-based records with inactive owners.  This can help preserve the original ownership of records that belong to users who are no longer active in Salesforce 8 .

Enable Set Audit fields upon Record Creation Permission: This permission allows users to set audit fields (such as Created By or Last Modified By) when they create a record via API importing tools like Data Loader.  This can help preserve the original date timestamps of records that were created or modified in another system 9 .

Log a case with SF to update these fields is not a good solution because it is not necessary or feasible to ask Salesforce support to update these fields manually or programmatically.  Enable modify all and view all permission is not a good solution because it does not affect the ability to preserve the date timestamps and ownership on records, but rather grants users access to all records regardless of sharing settings

Archiving and purging old data from Salesforce on a monthly basis is a good strategy to improve the user experience for the Sales Reps, as it will reduce the clutter and improve the search performance. Creating a permission set or setting data access to private are not effective ways to hide old data from Sales Reps, as they will still consume data storage and affect search results. Using Batch Apex to archive old data on a rolling nightly basis is also not a good option, as it will consume API requests and processing time, and may not comply with the data retention policy.

 Using a validation rule for each field that takes the Stage into consideration is the simplest approach to handle this new requirement. A validation rule can enforce the field requirements based on the logic and criteria that you define, and display an error message when users try to save a record that does not meet the requirements. Updating the Opportunity page layout to mark these fields as required will not work because page layouts do not support conditional field requirements. Updating these Opportunity field definitions in Setup to be required will not work because it will apply to all stages and records.  Writing an Apex trigger that checks each field when records are saved is not the simplest approach because it requires coding and testing

Leveraging duplicate rules in Salesforce to validate duplicates during the account creation process (option A) is the best option to implement to avoid duplicates, as it allows the sales associates to identify and merge duplicate accounts before they are saved. Developing an Apex class that searches for duplicates and removes them nightly (option B) is not a good option, as it may cause data loss or conflicts, and it does not prevent duplicates from being created in the first place. Implementing an MDM solution to validate the customer information before creating Salesforce (option C) is also not a good option, as it may introduce additional complexity and cost, and it does not address the issue of customers interacting with both the website and the store. Building a custom search functionality that allows sales associates to search for customer in real time upon visiting their retail stores (option D) is also not a good option, as it may not be reliable or user-friendly, and it does not leverage the existing Global Search feature.

The correct answer is C, leverage Salesforce Connect to bring the order line item from the legacy system to Salesforce. Salesforce Connect is a feature that allows you to integrate external data sources with Salesforce and access them in real time without copying or synchronizing the data. This way, the sales team can view the order status from the ERP system without having access to it. Leveraging Canvas, building batch jobs, or building real-time integration are also possible solutions, but they are more complex and costly than using Salesforce Connect.

Creating a custom lookup field on the standard Product to reference the parent record in the hierarchy is the correct way to modify the standard Product object model to support a hierarchical data structure. This allows UC to create a self-relationship on the Product object and define parent-child relationships among products.

Implicit sharing will automatically handle this sharing requirement with standard functionality, as it grants read-only access to parent accounts when users have access to child opportunities.  This is also known as account-opportunity sharing 3 . Creating a criteria-based sharing rule (option A) or an owner-based sharing rule (option D) is not necessary, as they are used to grant additional access based on record criteria or ownership. Adding appropriate users to the account team (option C) is also not required, as it is used to grant access to specific users or groups for individual accounts.

The three considerations needed prior to a SF Connect implementation are to develop an object relationship strategy, identify the external tables to sync into external objects, and assess whether the external data source is reachable via an ODATA endpoint. SF Connect is a feature that allows integrating external data sources with Salesforce using external objects. External objects are similar to custom objects, but they store metadata only and not data. They enable on-demand access to external data via standard Salesforce APIs and user interfaces. To implement SF Connect, a data architect needs to consider how the external objects will relate to other objects in Salesforce, which external tables will be exposed as external objects, and whether the external data source supports ODATA protocol for data access.

The correct answer is B. To make changes efficiently, a data architect should enable Defer Sharing Calculation prior to making sharing rule changes. This will allow the administrator to make multiple changes to sharing rules without recalculating them after each change, which can take a long time and cause failures. The sharing rules can be recalculated later when there are fewer users online or during off-peak hours. Option A is incorrect because logging a case with salesforce to make sharing rule changes will not speed up the process or prevent failures. Option C is incorrect because deleting old sharing rules and building new sharing rules will not reduce the processing time or failure rate. Option D is incorrect because logging out all users and making changes to sharing rules will disrupt the business operations and may not be feasible.

Checking if the current user is NOT the owner by creating a “before delete” trigger can meet the requirement of allowing only the owner of a contact record to delete that contact. A trigger is a piece of Apex code that can execute before or after a record is inserted, updated, deleted, or undeleted. A “before delete” trigger can prevent the deletion of a record by using the  addError()  method.

According to  this article , the Apex Connector Framework enables developers to create custom adapters for Salesforce Connect to access data from external systems that are not OData enabled. This can meet UC’s requirements of exposing shipment details to the custom application and accessing the data in real time.

An Outbound Message is a native Salesforce feature that allows sending data to an external system without code.  It can be configured to include any fields from the source object 3 . A Field Set is a collection of fields that can be used in Visualforce pages or Apex classes to dynamically determine the fields to send in an HTTP callout. Both of these approaches meet the requirements of Cloud Kicks.

Utilizing an app from the AppExchange to create data-quality dashboards is the approach that the architect should recommend for UC to implement a data-quality process to monitor the data that users are manually entering into the system through the Salesforce UI. An app from the AppExchange can provide ready-made or customizable dashboards that can display various metrics and indicators of data quality, such as completeness, accuracy, consistency, timeliness, etc. These dashboards can help UC to measure and evaluate their data quality performance and identify any areas that need improvement or attention. The other options are not relevant or effective for implementing a data-quality process, as they would either not address the issue of manual data entry, not provide data-quality monitoring, or not leverage the benefits of Salesforce.

The correct answer is A, D, and E. The data architect should consider these three factors when evaluating the need for Salesforce Connect: You want real-time access to the latest data from other systems, you have a large amount of data that you don’t want to copy into your Salesforce org, and you need to small amounts of external data at any one time. These factors indicate that Salesforce Connect is a suitable solution for creating a connected experience in Salesforce without importing inventory data from a cloud database. Salesforce Connect allows Salesforce to access external data via OData or custom adapters without storing it in Salesforce, which reduces storage costs and ensures data freshness. Salesforce Connect also supports pagination and caching to optimize performance when accessing small amounts of external data at any one time. Option B is incorrect because if you have a large amount of data and would like to copy subsets of it into Salesforce, you may not need Salesforce Connect but rather use other tools such as Data Loader or API integration. Option C is incorrect because if you need to expose data via a virtual private connection, you may not need Salesforce Connect but rather use other tools such as VPN or VPC peering.

Using Duplicate Management and Data.com Clean are two recommended approaches for UC to avoid duplicate data and increase the level of data quality. Duplicate Management can prevent or alert users when they try to create or edit records that are duplicates of existing records. Data.com Clean can compare Salesforce records with Data.com records and provide suggestions for updates or removals of duplicate records.

PK Chunking is a feature of the Bulk API that allows splitting a large query into smaller batches based on the primary key of the object.  This can improve the performance and avoid query timeouts when querying large data sets

 Salesforce allows up to four objects in a single report type. This means that if an architect wants to create a report that compiles data from Accounts, Contacts, Opportunities, Orders, and Order Line Items, they will run into a difficulty because that would require five objects in a single report type. To overcome this limitation, the architect can either create two separate report types and join them in a joined report, or use an external reporting tool that can access data from multiple objects.

Utilizing a Validation Rule with a REGEX operator on Postal Code and utilizing a Validation Rule with a CONTAINS operator on address fields are two actions that Cloud Kicks should take to improve data quality for their Invoice records. A Validation Rule with a REGEX operator can check if the Postal Code field matches a specific pattern or format, such as a five-digit number or a combination of letters and numbers. A Validation Rule with a CONTAINS operator can check if the address fields contain certain values, such as valid state abbreviations or country names. These Validation Rules can prevent users from saving invalid or incomplete data and display error messages to guide them to correct the data.  The other options are not effective or recommended for improving data quality, as they would either require additional customization, not enforce data standards, or not address the specific issues of missing states and incorrectly formatted postal codes

The Sforce-Enable-PKChunking header enables you to extract large data sets from Salesforce by using a technique called primary key (PK) chunking. PK chunking splits your query into multiple queries based on the record IDs of the queried object.  This reduces the query time and avoids the query timeout issues that can occur with large data volumes

 Option C is the best answer because reviewing projected data storage needs is an important step in designing a field-level audit and recovery plan 1 . You need to estimate how much data storage you will need in the future and plan accordingly. Option A is not correct because reducing data storage by purging old data may not be sufficient or desirable for backup purposes. Option B is not correct because implementing an AppExchange package may not be customized or compatible with your org’s requirements. Option D is not correct because scheduling a weekly export file may not be frequent or granular enough for field-level audit and recovery.

According to Trailhead 2 , one of the best practices to increase Salesforce adoption is to migrate existing data from legacy systems or spreadsheets into Salesforce, so that users can access all their data in one place and leverage the features and functionality of Salesforce. Option B is the correct answer because it suggests defining a standard mapping and training sales users to import opportunity data from Excel files into Salesforce, which can help them transition from their old process and increase their confidence and satisfaction with Salesforce. Option A is incorrect because using the Excel connector to Salesforce does not migrate the data into Salesforce, but only syncs it between Excel and Salesforce, which can cause data inconsistency and duplication issues. Option C is incorrect because loading data in an external database and providing access to it does not increase Salesforce adoption, but rather creates another system for users to manage and switch between. Option D is incorrect because creating a chatter group and uploading all Excel files to it does not migrate the data into Salesforce, but only stores it as attachments, which cannot be used for reporting or analysis purposes.

The correct answer is C, migrate Notes and Attachment to Enhanced Notes and Files using a migration tool. Enhanced Notes and Files are the new features in Lightning Experience that replace the classic Notes and Attachments. They offer more functionality and security than the classic version. To access them in Lightning Experience, you need to migrate your existing Notes and Attachments using a migration tool provided by Salesforce. Adding Notes and Attachments Related List, manually uploading Notes or Attachments, or doing nothing are not valid solutions, as they will not enable you to use the enhanced features in Lightning Experience.

Using Salesforce Bulk API in parallel mode can reduce the migration time by processing multiple batches of records simultaneously and leveraging the server resources more efficiently.  The Bulk API is designed for loading large amounts of data into Salesforce

 creating multiple similar products and distributing the skew across those products can be a way to mitigate issues related to lookup skew. The article explains that lookup skew happens when a very large number of records are associated with a single record in the lookup object, and this can cause record locking and performance issues. The article suggests creating multiple copies of the same product record and assigning different child records to each copy, so that the number of child records per parent record is reduced.

According to the  Get Started with Custom Metadata Types  unit on Trailhead, one of the use cases for custom metadata types is to define custom charges for an accounting app. The unit states that “Say that your org uses a standard accounting app. You can create a custom metadata type that defines custom charges, like duties and VAT rates. Then you can write some Apex code that calculates the total amount due for each invoice by using the metadata from your custom metadata type.” Therefore, a similar approach can be used to maintain the pricing criteria for a custom pricing engine in Salesforce.

The two tasks required to define the data archive strategy are to identify how the archive data will be accessed and used, and identify the data retention requirements. Data archiving is the process of moving infrequently used or historical data from active storage to a separate storage location for long-term retention. Data archiving can improve performance, reduce storage costs, and comply with legal or regulatory obligations. To define a data archive strategy, a data architect needs to consider how the archived data will be accessed and used by different users or processes in Salesforce or outside Salesforce, and how long the archived data needs to be retained based on business or legal requirements.

Setting up a Validation Rule to prevent blank values and marking the field required in setup at the field level are two approaches that the Architect should take to ensure that the field always contains data coming from the source system. A Validation Rule can display an error message when a record is created or edited with a blank value for the field, and prevent it from being saved. Marking the field required in setup at the field level can enforce the requirement for all records, regardless of the page layout or the source system.  The other options are not effective or recommended for ensuring data quality, as they would either not prevent blank values, not apply to all records, or not address the root cause of the problem

According to the Salesforce documentation 1 , reporting performance can be affected by various factors, such as the volume and complexity of data, the design and configuration of reports and dashboards, the number and type of users accessing the reports, etc. To improve reporting performance, some of the recommended solutions are:

Move data off of the platform and run reporting outside Salesforce, and give access to reports (option C). This means using an external service or tool that can extract, transform, and load (ETL) data from Salesforce to another system or database, such as a data warehouse or a business intelligence platform. This can improve reporting performance by reducing the load and latency on Salesforce, and enabling faster and more flexible reporting and analysis on the external system. Users can access the reports from the external system using a link or an embedded component in Salesforce.

Contact Salesforce support to enable skinny table for cases (option B). This means requesting Salesforce to create a custom table that contains a subset of fields from the Case object that are frequently used or queried.  A skinny table can improve reporting performance by avoiding joins between standard and custom fields, omitting soft-deleted records, and leveraging indexes on the fields 2 .

Create a custom object to store aggregate data and run reports (option A). This means creating a custom object that contains summary or calculated data from the Case object, such as counts, sums, averages, etc. A custom object can improve reporting performance by reducing the number of records and fields that need to be queried and displayed.

Build reports using custom Lightning components (option D). This means creating custom components that use Lightning Web Components or Aura Components frameworks to display report data in Salesforce. A custom component can improve reporting performance by using client-side caching, pagination, lazy loading, or other techniques to optimize data rendering and interaction.

The data architect should recommend building a batch job to move two-year-old records off platform, and delete records from Salesforce as an archiving solution. A batch job is a process that runs in the background and performs operations on large volumes of data in Salesforce. By building a batch job that moves two-year-old records off platform to an external storage system, such as Amazon S3 or Google Cloud Storage, and deletes them from Salesforce, the data architect can reduce the storage consumption and improve the performance of Salesforce org. Option A is incorrect because using a third-party backup solution to backup all data off platform will not free up any storage space in Salesforce, unless the data is also deleted from Salesforce after backup. Option B is incorrect because building a batch job to move all records off platform, and delete all records from Salesforce will result in losing all the current data in Salesforce, which may not be desirable or feasible. Option D is incorrect because building a batch job to move all restore off platform, and delete old records from Salesforce does not make sense, as restore implies restoring data back to Salesforce, not moving it off platform.

To allow only the owner of a contact record to delete that contact, the data architect should create a “before delete” trigger to check if the current user is not the owner. The trigger can use the UserIn fo.getUserId() method to get the current user’s ID and compare it with the OwnerId field of the contact record. If they are not equal, the trigger can add an error to the record and prevent it from being deleted. The other options are not suitable for meeting the requirements, as they would either restrict the edit access or delete access for all users, regardless of ownership.