Databricks Databricks-Certified-Data-Engineer-Professional Value Pack (Frequently Bought Together)

A data engineer wants to automate job monitoring and recovery in Databricks using the Jobs API.
They need to list all jobs, identify a failed job, and rerun it. Which sequence of API actions should the data engineer perform?

• A. Use the jobs/list endpoint to list jobs, then use the jobs/create endpoint to create a new job, and run the new job using jobs/run-now.
• B. Use the jobs/list endpoint to list jobs, check job run statuses with jobs/runs/list, and rerun a failed job using jobs/run-now.
• C. Use the jobs/get endpoint to retrieve job details, then use jobs/update to rerun failed jobs.
• D. Use the jobs/cancel endpoint to remove failed jobs, then recreate them with jobs/create and run the new ones.

Explanation: Only visible for PracticeTorrent members. You can sign-up / login (it's free).

A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

• A. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
• B. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
• C. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
• D. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
• E. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.

Explanation: Only visible for PracticeTorrent members. You can sign-up / login (it's free).

A data engineer is masking a column containing email addresses. The goal is to produce output strings of identical length for all rows, while generating different outputs for different email values.
Which SQL function should be used to achieve this?

• A. sha2(email, 0)
• B. sha1(email)
• C. hash(email)
• D. mask(email, '?')

Explanation: Only visible for PracticeTorrent members. You can sign-up / login (it's free).

A data engineer is brining an existing production Databricks job under asset bundle management and wants to ensure that:
- The job's current configuration is captured as YAML, and all
referenced files are included in their bundle project.
- Future changes to the bundle's YAML will update the existing job in-
place (not create a new job)
How should the data engineer successfully move the production job under asset bundle management?

• A. Run Databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deploy to deploy the bundle, which will always update the existing job automatically.
• B. Export the job definition as JSON, convert it to YAML, and place it in your bundle. Then, run Databricks bundle deploy to update the existing job.
• C. Run databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deployment, bind to link the bundle's job resource to the existing job in Databricks.
• D. Manually create the YAML configuration for the job in your bundle project, ensuring all settings match the existing job. Then, run Databricks bundle deploy the bundle, which will update the existing job in your workspace.

Explanation: Only visible for PracticeTorrent members. You can sign-up / login (it's free).

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 view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table
• B. 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
• C. 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.
• 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

Explanation: Only visible for PracticeTorrent members. You can sign-up / login (it's free).