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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

The Databricks CLI is use to trigger a run of an existing job by passing the job_id parameter. The response that the job run request has been submitted successfully includes a filed run_id.
Which statement describes what the number alongside this field represents?

A. The total number of jobs that have been run in the workspace.
B. The globally unique ID of the newly triggered run.
C. The number of times the job definition has been run in the workspace.
D. The job_id is returned in this field.
E. The job_id and number of times the job has been are concatenated and returned.

Question 2

An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

A. Shallow clone the table for the analytics team.
B. Give the analytics team direct access to the production table.
C. Deep clone the table for the analytics team.
D. Create a new table for the analytics team using a CTAS statement.

Question 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 job run my_project_job --env prod
B. databricks execute my_project_job -e prod
C. databricks bundle run my_project_job -t prod
D. databricks run my_project_job -t prod

Question 4

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. 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.
B. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
C. 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.
D. 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.
E. 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.

Question 5

A transactions table has been liquid clustered on the columns product_id, user_id, and event_date. Which operation lacks support for cluster on write?

A. INSERT INTO operations
B. spark.writestream.format('delta').mode('append')
C. CTAS and RTAS statements
D. spark.write.format('delta').mode('append')

Solutions: