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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Developing Code for Data Processing using Python and SQL | ~22% | - Manage dependencies, libraries, and UDFs - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Implement scalable Python/SQL code and project structures |
| Monitoring, Logging, and Troubleshooting | ~8% | - Use Spark UI, Query Profiler, and system tables - Diagnose common pipeline and job failures |
| Security and Governance | ~10% | - Implement row-level security, column masking, and compliance - Manage Unity Catalog permissions and ACLs |
| Streaming Workloads and Change Data Capture | ~11% | - Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines |
| Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
| CI/CD, Testing, and Deployment | ~6% | - Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines |
| Cost and Performance Optimization | ~13% | - Optimize queries, clusters, and storage - Leverage system tables and observability tools |
| Data Modeling | ~10% | - Design scalable Delta Lake schemas and clustering - Apply dimensional modeling techniques |
| Data Transformation, Cleansing, and Quality | ~12% | - Enforce data quality and quarantine bad data - Apply advanced Spark transformations |
Databricks Certified Data Engineer Professional Sample Questions:
A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays.
Which limitation will the team face while diagnosing this problem?
- A. New fields will not be computed for historic records.
- B. Spark cannot capture the topic partition fields from the kafka source.
- C. Updating the table schema requires a default value provided for each file added.
- D. Updating the table schema will invalidate the Delta transaction log metadata.
- E. New fields cannot be added to a production Delta table.
Correct Answer: A 🗳️
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Review the following error traceback:
Which statement describes the error being raised?
- A. There is no column in the table named heartrateheartrateheartrate
- B. There is a type error because a DataFrame object cannot be multiplied.
- C. There is a syntax error because the heartrate column is not correctly identified as a column.
- D. There is a type error because a column object cannot be multiplied.
- E. The code executed was PvSoark but was executed in a Scala notebook.
Correct Answer: A 🗳️
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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. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
- B. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
- C. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
- 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 registration field, allowing ACLs and delete statements to be set for the PII directory.
Correct Answer: B 🗳️
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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. spark.write.format('delta').mode('append')
- C. CTAS and RTAS statements
- D. spark.writestream.format('delta').mode('append')
Correct Answer: D 🗳️
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The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?
- A. Can Edit
- B. Can Manage
- C. No permissions
- D. Can Run
- E. Can Read
Correct Answer: E 🗳️
