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

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is designing a secure data sharing strategy for their organization. The company needs to share sensitive customer analytics data with two different partners. Partner A uses Databricks with Unity Catalog enabled, while Partner B uses Apache Spark on AWS without Databricks. How should the company implement secure data sharing for these scenarios?

A) Open sharing protocol (D2O) should be used for both partners because it provides better security than D2D sharing. The bearer token approach is always more secure than Unity Catalog's native authentication.
B) For Partner A, implement Databricks-to-Databricks sharing (D2D) with Unit Catalog integration and no-token exchange system. For Partner B, use open sharing protocol (D2O) with either bearer tokens or OIDC federation for authentication, ensuring both approaches maintain robust security and governance.
C) Both partners should use the same Delta Sharing approach since security requirements are identical. You should create bearer tokens for both partners and use the open sharing protocol (D2O) for maximum compatibility.
D) Databricks-to-Databricks sharing (D2D) can only be used within the same cloud provider, so you must use open sharing (D2O) for any cross-cloud scenarios. Unit Catalog governance is not available when sharing with external platforms.

2. A data engineer is optimizing a managed Delta table that suffers from data skew and frequently changing query filter columns. The engineer wants to avoid costly data rewrites when query patterns evolve. The table size is under 1 TB. How should the data engineer meet this requirement?

A) Enable liquid clustering, as it efficiently handles data skew, allows clustering keys to be changed without rewriting existing data, and adapts to evolving query patterns.
B) Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.
C) Use Hive-style partitioning, as it provides efficient data skipping and is easy to change partition columns at any time.
D) Apply Z-ordering, since it allows flexible reorganization of data layout without rewriting existing files and adapts easily to new filter columns.

3. A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:

Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

A) No; only one stream can write to a Delta Lake table.
B) No; each of the streams needs to have its own checkpoint directory.
C) Yes; both of the streams can share a single checkpoint directory.
D) Yes; Delta Lake supports infinite concurrent writers.
E) No; Delta Lake manages streaming checkpoints in the transaction log.

4. Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

A) Validates a complete use case of your application
B) Ensures that all steps interact correctly to achieve the desired end result
C) Yields faster deployment and execution times
D) Improves the quality of your data
E) Troubleshooting is easier since all steps are isolated and tested individually

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 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
B) 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
C) Stop the existing pipeline; use the returned settings in a reset command
D) 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

Solutions: