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

Databricks Certified Data Engineer Professional Sample Questions:

1. A job runs four independent tasks (X, Y, Z, W) in parallel to process regional sales data. The Data Engineering team recently updated its cluster policy to ban cost-prohibitive instance types. Task Y now fails due to the newly enforced cluster policy restricting the use of a specific instance type.
A data engineer needs to resolve the failure quickly without disrupting the other tasks. How should the data engineer resolve the failure of tasks?

A) Use "Repair run", override the cluster configuration for Task Y to use a permitted instance type, and let Databricks re-run only Task Y.
B) Manually create a new cluster for Task Y, update the job configuration, and trigger a full re-run.
C) Delete the failed run, disable the cluster policy, and re-execute all tasks.
D) Edit the global cluster policy to allow the restricted instance type, then re-run the entire job.

2. A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data. Which configuration will provide the optimal performance while enabling these capabilities?

A) Share the entire schema WITHOUT HISTORY and rely on recipient-side caching for performance.
B) Share tables WITH HISTORY, ensure tables don't have partitioning enabled, and enable CDF before sharing.
C) Share tables WITHOUT HISTORY and enable partitioning for better query performance.
D) Use the open sharing protocol instead of Databricks-to-Databricks sharing for better performance.

3. A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A) Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
B) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
C) All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
D) The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
E) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.

4. A data organization has adopted Delta Sharing to securely distribute curated datasets from a Unity Catalog-enabled workspace. The data engineering team shares large Delta tables internally via Databricks-to-Databricks and externally via Open Sharing for aggregated reports. While testing, they encounter challenges related to access control, data update visibility, and shareable object types. What is a limitation of the Delta Sharing protocol or implementation when used with Databricks-to-Databricks or Open Sharing?

A) Delta Sharing (both Databricks-to-Databricks and Open Sharing) allows recipients to modify the source data if they have select privileges.
B) With Databricks-to-Databricks sharing, Unity Catalog recipients must re-ingest data manually using COPY INTO or REST APIs.
C) With Open Sharing, recipients cannot access Volumes, Models, or notebooks -- only static Delta tables are supported.
D) Delta Sharing does not support Unity Catalog-enabled tables; only legacy Hive Metastore tables are shareable.

5. In a Databricks Asset Bundle project, in the file resources/app.yml, the data engineer would like to deploy a Databricks Apps databricks_app_deployed and Volume volume_deployed and grant the Service Principal behind Databricks Apps permissions to READ and WRITE to the Volume.
How should the data engineer achieve the deployment?

A)

B)

C)

D)

Solutions: