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Databricks Certified Professional Data Engineer Exam Sample Questions (Q117-Q122):

NEW QUESTION # 117
Which of the following statement is true about Databricks repos?

A. You can approve the pull request if you are the owner of Databricks repos
B. Databricks Repos and Notebook versioning are the same features
C. Databricks repos allow you to comment and commit code changes and push them to a remote branch
D. You cannot create a new branch in Databricks repos
E. A workspace can only have one instance of git integration

Answer: C

Explanation:
Explanation
See below diagram to understand the role Databricks Repos and Git provider plays when building a CI/CD workdlow.
All the steps highlighted in yellow can be done Databricks Repo, all the steps highlighted in Gray are done in a git provider like Github or Azure Devops Diagram Description automatically generated

NEW QUESTION # 118
A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline.
How should the data engineer perform this syntax check?

A. Switch to a workspace file instead of a notebook to access validation and diagnostics tools.
B. Use the "Validate" option in the notebook to check for syntax errors.
C. Open the web terminal from the notebook and run a shell command to validate the pipeline code.
D. Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.

Answer: B

Explanation:
Databricks provides a "Validate" option within the Lakeflow Declarative Pipeline development interface that checks pipeline configurations, transformations, and syntax errors before actual execution.
This feature parses and validates the pipeline logic defined in notebooks or workspace files to ensure correctness and consistency of table dependencies, DLT (Delta Live Table) syntax, and schema references.
The validation process does not process or move any data , making it ideal for testing new configurations before deployment.
Using the shell terminal (B) or workspace files (D) does not perform integrated pipeline-level validation, while reconnecting to compute clusters (C) is unrelated to syntax checks. Therefore, the verified and correct approach is A .

NEW QUESTION # 119
A data engineering manager has noticed that each of the queries in a Databricks SQL dashboard takes a few
minutes to update when they manually click the "Refresh" button. They are curious why this might be
occurring, so a team member provides a variety of reasons on why the delay might be occurring.
Which of the following reasons fails to explain why the dashboard might be taking a few minutes to update?

A. The queries attached to the dashboard might first be checking to determine if new data is available
B. The Job associated with updating the dashboard might be using a non-pooled endpoint
C. The queries attached to the dashboard might all be connected to their own, unstarted Databricks clusters
D. The queries attached to the dashboard might take a few minutes to run under normal circumstances
E. The SQL endpoint being used by each of the queries might need a few minutes to start up

Answer: B

NEW QUESTION # 120
Which of the following technique can be used to implement fine-grained access control to rows and columns of the Delta table based on the user's access?

A. Use dynamic view functions
B. Use Unity catalog to grant access to rows and columns
C. Row and column access control lists
D. Data access control lists
E. Dynamic Access control lists with Unity Catalog

Answer: A

Explanation:
Explanation
The answer is, Use dynamic view functions.
Here is an example that limits access to rows based on the user being part managers group, in the below view if a user is not a part of the manager's group you can only see rows where the total amount is <= 1000000 Dynamic view function to filter rows
1.CREATE VIEW sales_redacted AS
2.SELECT user_id, country, product, total
3.FROM sales_raw
4.WHERE CASE WHEN is_member('managers') THEN TRUE ELSE total <= 1000000 END; Dynamic view function to hide a column data based on user's access,
1.CREATE VIEW sales_redacted AS
2.SELECT user_id,
3. CASE WHEN is_member('auditors') THEN email ELSE 'REDACTED' END AS email,
4. country,
5. product,
6. total
7.FROM sales_raw
Please review below for more details
https://docs.microsoft.com/en-us/azure/databricks/security/access-control/table-acls/object-privileges#dynamic-v

NEW QUESTION # 121
A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?

A. Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.
B. Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table
C. Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
D. Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table

Answer: B

Explanation:
To validate that all records from the source are included in the derived table, creating a view that performs a left outer join between the validation_copy table and the report table is effective. The view can highlight any discrepancies, such as null values in the report table's key columns, indicating missing records. This view can then be referenced in DLT (Delta Live Tables) expectations for the report table to ensure data integrity. This approach allows for a comprehensive comparison between the source and the derived table.
Reference:
Databricks Documentation on Delta Live Tables and Expectations: Delta Live Tables Expectations

NEW QUESTION # 122
......

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