Databricks Certified-Data-Engineer-Professional : Databricks Certified Data Engineer Professional

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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A) When the workspace is being configured, make sure that external cloud object storage has been mounted.
B) When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
C) When a database is being created, make sure that the LOCATION keyword is used.
D) When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.
E) When data is saved to a table, make sure that a full file path is specified alongside the Delta format.

2. A data engineer needs to implement column masking for a sensitive column in a Unity Catalog- managed table. The masking logic must dynamically check if users belong to specific groups defined in a separate table (group_access) that maps groups to allowed departments. Which approach should the engineer use to efficiently enforce this requirement?

A) Create a UDF that hardcodes allowed groups and apply it as a column mask.
B) Apply a column mask that references the group_access mapping table in its UDF.
C) Use a row filter to restrict access based on the user's group.
D) Create a view without selecting the sensitive column.

3. A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context.
Assuming the job is already deployed, they need to trigger its execution while ensuring the target- specific configuration is respected. Which command will trigger the job execution?

A) databricks execute my_project_job -e prod
B) databricks run my_project_job -t prod
C) databricks job run my_project_job --env prod
D) databricks bundle run my_project_job -t prod

4. The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

A) No; the change data feed only tracks inserts and updates not deleted records.
B) No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
C) Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
D) No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
E) Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.

5. A data engineer deploys a multi-task Databricks job that orchestrates three notebooks. One task intermittently fails with Exit Code 1 but succeeds on retry. The engineer needs to collect detailed logs for the failing attempts, including stdout/stderr and cluster lifecycle context, and share them with the platform team. What steps the data engineer needs to follow using built-in tools?

A) Download worker logs directly from the Spark UI and ignore driver logs, as worker logs contain stdout/stderr for all tasks and cluster events.
B) From the job run details page, export the job's logs or configure log delivery; then retrieve the compute driver logs and event logs from the compute details page to correlate stdout/stderr with cluster events.
C) Export the notebook run results to HTML; this bundle includes complete stdout, stderr, and cluster event history across all tasks.
D) Use the notebook interactive debugger to re-run the entire multi-task job, and capture step- through traces for the failing task.

Solutions: