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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Modeling and Transformation | - Performance optimization techniques - Spark SQL transformations - Dimensional modeling concepts |
| Topic 2: Delta Lake and Data Management | - Delta Lake transactions and ACID properties - Time travel and versioning - Schema evolution and enforcement |
| Topic 3: Production Pipelines and Orchestration | - Job scheduling and monitoring - Error handling and recovery strategies - Databricks Workflows |
| Topic 4: Databricks Lakehouse Platform Architecture | - Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold) - Workspace and cluster architecture |
| Topic 5: Data Ingestion and Processing | - Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals - ETL pipeline design patterns |
Databricks Certified Data Engineer Professional Sample Questions:
A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?
- A. Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table
- B. Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
- C. Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table
- D. Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.
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Which statement describes Delta Lake Auto Compaction?
- A. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
- B. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
- C. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
- D. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
- E. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
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A company stores account transactions in a Delta Lake table. The company needs to apply frequent account-level correlations (e.g., UPDATE statements) but wants to avoid rewriting entire Parquet files for each change to reduce file churn and improve write performance. Which Delta Lake feature should they enable?
- A. Enable change data feed on the Delta table
- B. Enable deletion vectors on the Delta table
- C. Enable automatic file compaction on writes
- D. Partition the Delta table by account_id
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A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day. At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.
Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?
- A. Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.
- B. Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish.
- C. Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch.
- D. The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism.
- E. Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped.
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A data team is automating a daily multi-task ETL pipeline in Databricks. The pipeline includes a notebook for ingesting raw data, a Python wheel task for data transformation, and a SQL query to update aggregates. They want to trigger the pipeline programmatically and see previous runs in the GUI. They need to ensure tasks are retried on failure and stakeholders are notified by email if any task fails. Which two approaches will meet these requirements? (Choose two.)
- A. Create a multi-task job using the UI, Databricks Asset Bundles (DABs), or the Jobs REST API (/jobs/create) with notebook, Python wheel, and SQL tasks. Configure task-level retries and email notifications in the job definition.
- B. Use the REST API endpoint /jobs/runs/submit to trigger each task individually as separate job runs and implement retries using custom logic in the orchestrator.
- C. Trigger the job programmatically using the Databricks Jobs REST API (/jobs/run-now), the CLI (databricks jobs run-now), or one of the Databricks SDKs.
- D. Use Databricks Asset Bundles (DABs) to deploy the workflow, then trigger individual tasks directly by referencing each task's notebook or script path in the workspace.
- E. Create a single orchestrator notebook that calls each step with dbutils.notebook.run(), defining a job for that notebook and configuring retries and notifications at the notebook level.
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