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Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Securing Data	8%	- Security and Access Control 1. Unity Catalog Security 2. Data Access Policies 3. Permissions and Roles
Developing AI/BI Genie Spaces	12%	- AI and BI Features 1. AI/BI Genie 2. Workspace Configuration 3. Natural Language Queries
Executing Queries Using Databricks SQL and SQL Warehouses	20%	- SQL Querying 1. Writing SQL Queries 2. SQL Warehouses 3. Filtering and Aggregation 4. Query Optimization 5. Joins and Subqueries
Analyzing Queries	15%	- Query Analysis 1. Monitoring Query Execution 2. Reading Query Plans 3. Performance Tuning
Importing Data	5%	- Data Import 1. Creating Tables from Imported Data 2. Uploading Files 3. Loading CSV and JSON Data
Understanding of Databricks Data Intelligence Platform	11%	- Platform Components 1. Unity Catalog 2. Mosaic AI 3. Delta Lake 4. Databricks SQL 5. Lakeflow Jobs - Catalog and Governance 1. Managed and External Tables 2. Catalogs and Schemas 3. Views and Certified Tables 4. Data Lineage
Managing Data	8%	- Data Management 1. Cleaning Data with SQL 2. Handling Missing and Invalid Data 3. Tagging and Lineage 4. Discovering Data Assets
Creating Dashboards and Visualizations	16%	- Visualization 1. Sharing and Publishing 2. Dashboards 3. Alerts and Filters 4. Charts and Graphs
Data Modeling with Databricks SQL	5%	- Modeling Concepts 1. Data Organization 2. Fact and Dimension Tables 3. Views and Relationships

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
A data analyst has a managed table table_name in database database_name. They would now like to remove the table from the database and all of the data files associated with the table. The rest of the tables in the database must continue to exist.
Which of the following commands can the analyst use to complete the task without producing an error?

A. DROP TABLE database_name.table_name;
B. DROP DATABASE database_name;
C. DELETE TABLE table_name FROM database_name;
D. DROP TABLE table_name FROM database_name;
E. DELETE TABLE database_name.table_name;

Answer: A

NEW QUESTION # 17
A data analyst has been asked to provide a list of options on how to share a dashboard with a client. It is a security requirement that the client does not gain access to any other information, resources, or artifacts in the database.
Which of the following approaches cannot be used to share the dashboard and meet the security requirement?

A. Generate a Personal Access Token that is good for 1 day and share it with the client.
B. Download a PNG file of the visualizations in the dashboard and share them with the client.
C. Set a refresh schedule for the dashboard and enter the client's email address in the "Subscribers" box.
D. Take a screenshot of the dashboard and share it with the client.
E. Download the Dashboard as a PDF and share it with the client.

Answer: A

Explanation:
The approach that cannot be used to share the dashboard and meet the security requirement is D. Generating a Personal Access Token that is good for 1 day and sharing it with the client. This approach would give the client access to the Databricks workspace using the token owner's identity and permissions, which could expose other information, resources, or artifacts in the database1. The other approaches can be used to share the dashboard and meet the security requirement because:
A) Downloading the Dashboard as a PDF and sharing it with the client would only provide a static snapshot of the dashboard without any interactive features or access to the underlying data2.
B) Setting a refresh schedule for the dashboard and entering the client's email address in the "Subscribers" box would send the client an email with the latest dashboard results as an attachment or a link to a secure web page3. The client would not be able to access the Databricks workspace or the dashboard itself.
C) Taking a screenshot of the dashboard and sharing it with the client would also only provide a static snapshot of the dashboard without any interactive features or access to the underlying data4.
E) Downloading a PNG file of the visualizations in the dashboard and sharing them with the client would also only provide a static snapshot of the visualizations without any interactive features or access to the underlying data5. Reference:
1: Personal access tokens
2: Download as PDF
3: Automatically refresh a dashboard
4: Take a screenshot
5: Download a PNG file

NEW QUESTION # 18
The stakeholders.customers table has 15 columns and 3,000 rows of dat
a. The following command is run:

After running SELECT * FROM stakeholders.eur_customers, 15 rows are returned. After the command executes completely, the user logs out of Databricks.
After logging back in two days later, what is the status of the stakeholders.eur_customers view?

A. The view is not available in the metastore, but the underlying data can be accessed with SELECT * FROM delta. `stakeholders.eur_customers`.
B. The view has been dropped.
C. The view has been converted into a table.
D. The view remains available but attempting to SELECT from it results in an empty result set because data in views are automatically deleted after logging out.
E. The view remains available and SELECT * FROM stakeholders.eur_customers will execute correctly.

Answer: E

Explanation:
In Databricks, a view is a saved SQL query definition that references existing tables or other views. Once created, a view remains persisted in the metastore (such as Unity Catalog or Hive Metastore) until it is explicitly dropped.
Key points:
Views do not store data themselves but reference data from underlying tables.
Logging out or being inactive does not delete or alter views.
Unless a user or admin explicitly drops the view or the underlying data/table is deleted, the view continues to function as expected.
Therefore, after logging back in-even days later-a user can still run SELECT * FROM stakeholders.eur_customers, and it will return the same data (provided the underlying table hasn't changed).

NEW QUESTION # 19
A data analyst has developed a query that runs against a Delta table. They want help from the data engineering team to implement a series of tests to ensure the data returned by the query is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following operations could the data engineering team use to run the query and operate with the results in PySpark?

A. spark.table
B. spark.delta.table
C. There is no way to share data between PySpark and SQL.
D. SELECT * FROM sales
E. spark.sql

Answer: E

Explanation:
Option C is correct because the requirement is to run the SQL query and then operate on the results in PySpark. spark.sql(...) executes SQL text and returns a Spark DataFrame that can be used in PySpark tests.
spark.table(...) can load a table directly, but it does not run an arbitrary SQL query. Option A is SQL text by itself, not a PySpark operation. Official Databricks documentation states that a SparkSession can be used to execute SQL over tables, and Databricks notebook documentation specifically references using SQL inside Python with a spark.sql command.

NEW QUESTION # 20
Consider the following two statements:
Statement 1:

Statement 2:
Which of the following describes how the result sets will differ for each statement when they are run in Databricks SQL?

A. When the first statement is run, all rows from the customers table will be returned and only the customer_id from the orders table will be returned. When the second statement is run, only those rows in the customers table that do not have at least one match with the orders table on customer_id will be returned.
B. There is no difference between the result sets for both statements.
C. Both statements will fail because Databricks SQL does not support those join types.
D. The first statement will return all data from the customers table and matching data from the orders table. The second statement will return all data from the orders table and matching data from the customers table. Any missing data will be filled in with NULL.
E. When the first statement is run, only rows from the customers table that have at least one match with the orders table on customer_id will be returned. When the second statement is run, only those rows in the customers table that do not have at least one match with the orders table on customer_id will be returned.

Answer: E

Explanation:
Based on the images you sent, the two statements are SQL queries for different types of joins between the customers and orders tables. A join is a way of combining the rows from two table references based on some criteria. The join type determines how the rows are matched and what kind of result set is returned. The first statement is a query for a LEFT SEMI JOIN, which returns only the rows from the left table reference (customers) that have a match with the right table reference (orders) on the join condition (customer_id). The second statement is a query for a LEFT ANTI JOIN, which returns only the rows from the left table reference (customers) that have no match with the right table reference (orders) on the join condition (customer_id).
Therefore, the result sets for the two statements will differ in the following way:
* The first statement will return a subset of the customers table that contains only the customers who have placed at least one order. The number of rows returned will be less than or equal to the number of rows in the customers table, depending on how many customers have orders. The number of columns returned will be the same as the number of columns in the customers table, as the LEFT SEMI JOIN does not include any columns from the orders table.
* The second statement will return a subset of the customers table that contains only the customers who have not placed any order. The number of rows returned will be less than or equal to the number of rows in the customers table, depending on how many customers have no orders. The number of columns returned will be the same as the number of columns in the customers table, as the LEFT ANTI JOIN does not include any columns from the orders table.
The other options are not correct because:
* A. The first statement will not return all data from the customers table, as it will exclude the customers who have no orders. The second statement will not return all data from the orders table, as it will exclude the orders that have a matching customer. Neither statement will fill in any missing data with NULL, as they do not return any columns from the other table.
* C. There is a difference between the result sets for both statements, as explained above. The LEFT SEMI JOIN and the LEFT ANTI JOIN are not equivalent operations and will produce different outputs.
* D. Both statements will not fail, as Databricks SQL does support those join types. Databricks SQL supports various join types, including INNER, LEFT OUTER, RIGHT OUTER, FULL OUTER, LEFT SEMI, LEFT ANTI, and CROSS. You can also use NATURAL, USING, or LATERAL keywords to specify different join criteria.
* E. The first statement will not return only the customer_id from the orders table, as it will return all columns from the customers table. The second statement is correct, but it is not the only difference between the result sets.
References: JOIN | Databricks on AWS, JOIN - Azure Databricks - Databricks SQL | Microsoft Learn, array_join function | Databricks on AWS, Hints | Databricks on AWS

NEW QUESTION # 21
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