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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Production Pipelines and Orchestration | - Job scheduling and monitoring - Databricks Workflows - Error handling and recovery strategies |
| Data Ingestion and Processing | - Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals - ETL pipeline design patterns |
| Data Modeling and Transformation | - Performance optimization techniques - Spark SQL transformations - Dimensional modeling concepts |
| Delta Lake and Data Management | - Time travel and versioning - Schema evolution and enforcement - Delta Lake transactions and ACID properties |
| Databricks Lakehouse Platform Architecture | - Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics) - Workspace and cluster architecture |
Databricks Certified Data Engineer Professional Sample Questions:
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?
- A. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
- B. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.
- C. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
- D. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
- E. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
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Why are Pandas UDFs often preferred over traditional PySpark UDFs in performance-critical applications involving large datasets?
- A. They minimize memory usage by streaming each row individually through a lightweight Python wrapper, avoiding batch processing overhead.
- B. They eliminate the JVM-Python boundary by bypassing serialization entirely, thereby avoiding data conversion overhead.
- C. They allow row-level execution of functions in Python with native Spark optimization, removing the need for columnar execution.
- D. They leverage Apache Arrow to enable vectorized operations between the JVM and Python runtimes, reducing serialization costs and improving computational efficiency.
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When monitoring a complex workload, being able to see the query plan is critical to understanding what the workload is doing. Where can the visualization of the query plan be found?
- A. In the Spark UI, under the SQL/DataFrame tab
- B. In the Spart UI, under the Jobs tab
- C. In the Query Profiler, under the Stages tab
- D. In the Query Profiler, under Query Source
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What statement is true regarding the retention of job run history?
- A. It is retained for 60 days, after which logs are archived
- B. It is retained until you export or delete job run logs
- C. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
- D. It is retained for 90 days or until the run-id is re-used through custom run configuration
- E. It is retained for 60 days, during which you can export notebook run results to HTML
A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline. Which command should the data engineer enter in a web terminal configured with the Databricks CLI?
- A. Stop the existing pipeline; use the returned settings in a reset command
- B. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline
- C. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command
- D. Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git
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