Databricks-Certified-Professional-Data-Engineer Free Practice Test

Which statement regarding spark configuration on the Databricks platform is true?

1. A. Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.
2. B. When the same spar configuration property is set for an interactive to the same interactive cluster.
3. C. Spark configuration set within an notebook will affect all SparkSession attached to the same interactive cluster
4. D. The Databricks REST API can be used to modify the Spark configuration properties for an interactive cluster without interrupting jobs.

Correct Answer: A
When Spark configuration properties are set for an interactive cluster using the Clusters UI in Databricks, those configurations are applied at the cluster level. This means that all notebooks attached to that cluster will inherit and be affected by these configurations. This approach ensures consistency across all executions within that cluster, as the Spark configuration properties dictate aspects such as memory allocation, number of executors, and other vital execution parameters. This centralized configuration management helps maintain standardized execution environments across different notebooks, aiding in debugging and performance optimization.
References:
✑ Databricks documentation on configuring clusters: https://docs.databricks.com/clusters/configure.html

Which is a key benefit of an end-to-end test?

1. A. It closely simulates real world usage of your application.
2. B. It pinpoint errors in the building blocks of your application.
3. C. It provides testing coverage for all code paths and branches.
4. D. It makes it easier to automate your test suite

Correct Answer: A
End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real-world, user behavior, ensuring that the system as a whole operates correctly.
References:
✑ Software Testing: End-to-End Testing

A table named user_ltv is being used to create a view that will be used by data analysis on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:

An analyze who is not a member of the auditing group executing the following query:

Which result will be returned by this query?

1. A. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
2. B. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
3. C. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.
4. D. All records from all columns will be displayed with the values in user_ltv.

Correct Answer: A
Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.

The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

1. A. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
2. B. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
3. C. Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.
4. D. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.

Correct Answer: A
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.
References:
✑ Databricks documentation on using SQL views in Delta Lake: https://docs.databricks.com/delta/quick-start.html#sql-views

A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table -
Which of the following describes how results are generated each time the dashboard is updated?

1. A. The total count of rows is calculated by scanning all data files
2. B. The total count of rows will be returned from cached results unless REFRESH is run
3. C. The total count of records is calculated from the Delta transaction logs
4. D. The total count of records is calculated from the parquet file metadata
5. E. The total count of records is calculated from the Hive metastore

Correct Answer: C
https://delta.io/blog/2023-04-19-faster-aggregations-metadata/#:~:text=You can get the number,a given Delta table version.