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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer needs to install the PyYAML Python package within an air-gapped Databricks environment. The workspace has no direct internet access to PyPI. The engineer has downloaded the .whl file locally and wants it available automatically on all new clusters. Which approach should the data engineer use?

A) Upload the PyYAML .whl file to the user home directory and create a cluster-scoped init script to install it.
B) Set up a private PyPI repository and install via pip index URL.
C) Upload the PyYAML .whl file to a Unity Catalog Volume, ensure it's allow-listed, and create a cluster-scoped init script that installs it from that path.
D) Add the .whl file to Databricks Git Repos and assume automatic installation.

2. A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of information is critical to this decision?

A) Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.
B) Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
C) Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.
D) Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
E) Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.

3. An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A) Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.
B) Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.
C) Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
D) Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.
E) Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.

4. 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) CTAS and RTAS statements
C) spark.writestream.format('delta').mode('append')
D) spark.write.format('delta').mode('append')

5. A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline. Which command should the data engineer enter in a web terminal configured with the Databricks CLI?

A) Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline
B) Stop the existing pipeline; use the returned settings in a reset command
C) Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git
D) Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command

Solutions: