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Databricks Certified Data Engineer Professional Sample Questions:

1. The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

A) The connection to the external table will fail; the string "redacted" will be printed.
B) An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
C) The connection to the external table will succeed; the string value of password will be printed in plain text.
D) An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
E) The connection to the external table will succeed; the string "redacted" will be printed.

2. The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.

Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A) Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
B) No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.
C) No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
D) No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
E) Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.

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

4. A data engineering team is implementing an append-only data pipeline using Delta Lake, and wants to ensure that data is never modified or deleted once written. Which Delta Lake feature should the data engineer enable to prevent modifications to existing data?

A) Delta Time Travel
B) Delta OPTIMIZE
C) Delta APPEND_ONLY
D) Delta VACUUM

5. An organization processes customer data from web and mobile applications. Data includes names, emails, phone numbers, and location history. Data arrives both as batch files (from SFTP daily) and streaming JSON events (from Kafka in real-time).
To comply with data privacy policies, the following requirements must be met:
- Personally Identifiable Information (PII) such as email, phone
number, and IP address must be masked or anonymized before storage.
- Both batch and streaming pipelines must apply consistent PII
handling.
- Masking logic must be auditable and reproducible.
- The masked data must remain usable for downstream analytics.
How should the data engineer design a compliant data pipeline on Databricks that supports both batch and streaming modes, applies data masking to PII, and maintains traceability for audits?

A) Allow PII to be stored unmasked in Bronze for lineage tracking, then apply masking logic in Gold tables used for reporting.
B) Ingest both batch and streaming data using Lakeflow Declarative Pipelines, and apply masking via Unity Catalog column masks at read time to avoid modifying the data during ingestion.
C) Use Lakeflow Declarative Pipelines for batch and streaming ingestion, define a PII masking function, and apply it during Bronze ingestion before writing to Delta Lake.
D) Load batch data with notebooks and ingest streaming data with SQL Warehouses; use Unity Catalog column masks on Silver tables to redact fields after storage.

Solutions: