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

Databricks Certified Data Engineer Professional Sample Questions:

1. Which distribution does Databricks support for installing custom Python code packages?

A) sbt
B) jars
C) nom
D) CRAN
E) Wheels
F) CRAM

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) Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
D) 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.
E) Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.

3. What describes a primary technical challenge in ensuring consistent PII masking across all nodes in large-scale, distributed Databricks batch and streaming pipelines?

A) Masking functions must be standardized and managed through Unity Catalog, with enforcement applied across all relevant datasets to avoid any data inconsistency.
B) Dynamic data masking is applied only at rest, so it does not affect query performance.
C) PII masking is only required for direct identifiers.
D) Native masking in Databricks automatically synchronizes with all downstream external Databricks systems.

4. A data engineer wants to enforce the principle of least privilege when configuring ACLs for Databricks jobs in a collaborative workspace. Which approach should the data engineer use?

A) Assign users only the minimum permission level (e.g., CAN RUN or CAN VIEW) required for their role on each job.
B) Grant all users CAN MANAGE permission on all jobs to avoid access issues.
C) Grant CAN RUN permission to everyone and CAN MANAGE to a single admin group.
D) Use only folder-level permissions and avoid setting permissions on individual jobs.

5. A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against function that work with data in production?

A) Define and import unit test functions from a separate Databricks notebook
B) Run unit tests against non-production data that closely mirrors production
C) Define and unit test functions using Files in Repos
D) Define units test and functions within the same notebook

Solutions: