Databricks-Certified-Data-Analyst-Associate Databricks Certified Data Analyst Associate Exam Questions and Answers

External tables are tables that are defined in the Databricks metastore using the information stored in a cloud object storage location. External tables do not manage the data, but provide a schema and a table name to query the data. To create an external table, you can use the CREATE EXTERNAL TABLE statement and specify the object storage path to the LOCATION clause. For example, to create an external table named ext_table on a Parquet file stored in S3, you can use the following statement:

SQL

CREATE EXTERNAL TABLE ext_table (

col1 INT,

col2 STRING

)

STORED AS PARQUET

LOCATION 's3://bucket/path/file.parquet'

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

 Option A uses the  SELECT DISTINCT  statement to remove duplicate rows from the  table_bronze  and create a new table  table_silver  with the deduplicated data.  This is the correct way to deduplicate data using Spark SQL 1 2 . Option B simply inserts all the rows from  table_bronze  into  table_silver , without removing any duplicates. Option C is not a valid syntax for Spark SQL, as there is no  MERGE DEDUPLICATE  statement. Option D appends all the rows from  table_bronze  into  table_silver , without removing any duplicates. Option E overwrites the existing data in  table_silver  with the data from  table_bronze , without removing any duplicates.  References :  Delete Duplicate using SPARK SQL ,  Spark SQL - How to Remove Duplicate Rows

To share a Databricks SQL dashboard with stakeholders who do not have accounts in the Databricks deployment and ensure they are notified upon each refresh, the data analyst can add the stakeholders' email addresses to the dashboard's refresh schedule subscribers list. This approach allows the stakeholders to receive email notifications containing the latest dashboard updates without requiring them to have direct access to the Databricks workspace. This method is efficient and minimizes effort, as it automates the notification process and ensures stakeholders remain informed of the most recent data insights.​

According to the Databricks SQL documentation, when a data analyst clicks on the “Add visualization” button on the SQL Editor page, the default visualization type is  Bar Chart . This is because the result set has two columns: one of type STRING and one of type INT. The Bar Chart visualization automatically assigns the STRING column to the X-axis and the INT column to the Y-axis. The Bar Chart visualization is suitable for showing the distribution of a numeric variable across different categories.  References :  Visualization in Databricks SQL ,  Visualization types

When connecting Power BI and Tableau through Databricks Partner Connect, the system automates several steps to streamline the integration process:​

Personal Access Token Creation: Partner Connect generates a Databricks personal access token, which is essential for authenticating and establishing a secure connection between Databricks and the BI tools.​

ODBC Driver Installation: The appropriate ODBC driver is downloaded and installed. This driver facilitates communication between the BI tools and Databricks, ensuring compatibility and optimal performance.​

Configuration File Download: A configuration file tailored for the selected BI tool (Power BI or Tableau) is provided. This file contains the necessary connection details, simplifying the setup process within the BI tool.​

By automating these steps, Partner Connect ensures a seamless and efficient integration, reducing manual configuration efforts and potential errors.​

A Serverless SQL endpoint is a type of SQL endpoint that does not require a dedicated cluster to run queries. Instead, it uses a shared pool of resources that can scale up and down automatically based on the demand. This means that a Serverless SQL endpoint can start up much faster than a SQL endpoint that uses a cluster, and it can also save costs by only paying for the resources that are used. A Serverless SQL endpoint is suitable for ad-hoc queries and exploratory analysis, but it may not offer the same level of performance and isolation as a SQL endpoint that uses a cluster. Therefore, a data analyst should consider the trade-offs between speed, cost, and quality when choosing between a Serverless SQL endpoint and a SQL endpoint that uses a cluster.  References :  Databricks SQL endpoints ,  Serverless SQL endpoints ,  SQL endpoint clusters

 Databricks SQL is a service that allows users to query data in the lakehouse using SQL and create visualizations and dashboards 1 .  One of the common use cases for Databricks SQL is to combine data from different sources and formats into a single, comprehensive dataset that can be used for further analysis or reporting 2 .  For example, a data analyst can use Databricks SQL to join data from a CSV file and a Parquet file, or from a Delta table and a JDBC table, and create a new table or view that contains the combined data 3 . This can help simplify the data management and governance, as well as improve the data quality and consistency.  References :

Databricks SQL overview

Databricks SQL use cases

Joining data sources

In Databricks SQL, a subquery is a nested query within a larger SQL query that allows for the retrieval of data without the necessity of creating a table or view. This is particularly useful for simplifying complex queries by breaking them down into more manageable parts. Subqueries can be employed in various clauses such as SELECT, FROM, and WHERE to perform operations like filtering, transforming, and aggregating data on-the-fly. This flexibility enhances query efficiency and readability without the overhead of persisting intermediate results as separate tables or views.

 In the provided SQL query, the data analyst is trying to count the number of customers in each region. However, they made a mistake by not including the “GROUP BY” clause to group the results by region. Without this clause, the query will not return counts for each distinct region but rather an error or incorrect result.  References : The need for a GROUP BY clause in such queries can be understood from Databricks SQL documentation:  Databricks SQL .

I also noticed that you uploaded an image with your question. The image shows a snippet of an SQL query written in plain text on a white background. The query is attempting to select regions and count customers from a “customers” table and order the results by region. There’s no visible syntax highlighting or any other color - it’s monochromatic. The query is the same as the one in your question. I’m not sure why you included the image, but maybe you wanted to show me the exact format of your query. If so, you can also use code blocks to display formatted content such as SQL queries. For example, you can write:

SELECT region, count(*) AS number_of_customers

FROM customers

ORDER BY region;

This way, you can avoid uploading images and make your questions more clear and concise. I hope this helps. ????

The Owner field in the table page shows the current owner of the table and allows the owner to change it to another user or group. To change the ownership of the table, the owner can click on the Owner field and select the new owner from the drop-down list.  This will transfer the ownership of the table to the selected user or group and remove the previous owner from the list of table access control entries 1 . The other options are incorrect because:

A.  Removing the owner’s account from the Owner field will not change the ownership of the table, but will make the table ownerless 2 .

B.  Selecting All Users from the Owner field will not change the ownership of the table, but will grant all users access to the table 3 .

D.  Selecting the Admins group from the Owner field will not change the ownership of the table, but will grant the Admins group access to the table 3 .

E.  Removing all access from the Owner field will not change the ownership of the table, but will revoke all access to the table 4 .  References :

1 : Change table ownership

2 : Ownerless tables

3 : Table access control

4 : Revoke access to a table

 Data engineers are primarily responsible for building, managing, and optimizing data pipelines and architectures. They use Databricks Data Science and Engineering service to perform tasks such as data ingestion, transformation, quality, and governance. Data engineers may use Databricks SQL as a secondary service to query, analyze, and visualize data from the lakehouse, but this is not their main focus.  References :  Databricks SQL overview ,  Databricks Data Science and Engineering overview ,  Data engineering with Databricks

Variance is a statistical measure that quantifies the dispersion or spread of a set of values around their mean (central value). It is calculated by taking the average of the squared differences between each value and the mean of the dataset. A higher variance indicates that the data points are more spread out from the mean, while a lower variance suggests that they are closer to the mean. This measure is fundamental in statistics to understand the degree of variability within a dataset.​ Wikipedia Wikipedia+1Investopedia+1

The gold layer of the medallion architecture contains data that is highly refined and aggregated, and powers analytics, machine learning, and production applications. Data analysts typically use the gold layer to access data that has been transformed into knowledge, rather than just information. The gold layer represents the final stage of data quality and optimization in the lakehouse.  References :  What is the medallion lakehouse architecture?

 The SQL code provided in Option D is the correct way to create a view named  sales_by_employee  that will always stay up-to-date with the sales and employees tables. The code uses the  CREATE OR REPLACE VIEW  statement to define a new view that joins the sales and employees tables on the employee_id column. It selects the employee_name as sales_person and all sales for each employee, ensuring that the data entity/object is always up-to-date when new data are added to these tables.

Data analysts should consider all of these factors when working with PII data, as they may affect the data security, privacy, compliance, and quality. PII data is any information that can be used to identify a specific individual, such as name, address, phone number, email, social security number, etc. PII data may be subject to different legal and ethical obligations depending on the context and location of the data collection and analysis. For example, some countries or regions may have stricter data protection laws than others, such as the General Data Protection Regulation (GDPR) in the European Union. Data analysts should also follow the organization-specific best practices for PII data, such as encryption, anonymization, masking, access control, auditing, etc. These best practices can help prevent data breaches, unauthorized access, misuse, or loss of PII data.  References :

How to Use Databricks to Encrypt and Protect PII Data

Automating Sensitive Data (PII/PHI) Detection

Databricks Certified Data Analyst Associate

Databricks SQL uses ANSI SQL as its standard SQL dialect, which means it follows the SQL specifications defined by the American National Standards Institute (ANSI). This makes it easier to migrate existing SQL queries from other data warehouses or platforms that also use ANSI SQL or a similar dialect, such as PostgreSQL, Oracle, or Teradata.  By using ANSI SQL, Databricks SQL avoids surprises in behavior or unfamiliar syntax that may arise from using a non-standard SQL dialect, such as Spark SQL or Hive SQL 1 2 .  Moreover, Databricks SQL also adds compatibility features to support common SQL constructs that are widely used in other data warehouses, such as QUALIFY, FILTER, and user-defined functions 2 .  References :  ANSI compliance in Databricks Runtime ,  Evolution of the SQL language at Databricks: ANSI standard by default and easier migrations from data warehouses