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

Databricks Certified Data Engineer Professional Sample Questions:

1. 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) Native masking in Databricks automatically synchronizes with all downstream external Databricks systems.
C) Dynamic data masking is applied only at rest, so it does not affect query performance.
D) PII masking is only required for direct identifiers.

2. 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 analytical purposes, only the most recent value for each record needs to be recorded in the target Delta Lake table in the Lakehouse. 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 table, then propagate all changes throughout the system.
B) 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.
C) Iterate through an ordered set of changes to the table, applying each in turn to create the current state of the table, (insert, update, delete), timestamp of change, and the values.
D) Deduplicate records in each batch by pk_id and overwrite the target table.

3. 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) 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.
B) 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.
C) Allow PII to be stored unmasked in Bronze for lineage tracking, then apply masking logic in Gold tables used for reporting.
D) 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.

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) CTAS and RTAS statements
B) spark.write.format('delta').mode('append')
C) spark.writestream.format('delta').mode('append')
D) INSERT INTO operations

5. All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion.
However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

A) Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
B) Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
C) All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
D) Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
E) Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.

Solutions: