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Databricks Certified Data Engineer Professional Sample Questions:

1. The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

A) No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
B) "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.
C) "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
D) "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.

2. A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

A) Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
B) Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
C) Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
D) Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.

3. 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)

4. When monitoring a complex workload, being able to see the query plan is critical to understanding what the workload is doing. Where can the visualization of the query plan be found?

A) In the Spark UI, under the SQL/DataFrame tab
B) In the Query Profiler, under the Stages tab
C) In the Spart UI, under the Jobs tab
D) In the Query Profiler, under Query Source

5. A data engineering team has a time-consuming data ingestion job with three data sources. Each notebook takes about one hour to load new data. One day, the job fails because a notebook update introduced a new required configuration parameter. The team must quickly fix the issue and load the latest data from the failing source. Which action should the team take?

A) Share the analysis with the failing notebook owner so that they can fix it quickly.
B) Repair the run with the new parameter.
C) Repair the run with the new parameter, and update the task by adding the missing task parameter.
D) Update the task by adding the missing task parameter, and manually run the job.

Solutions: