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

Section	Weight	Objectives
Data Processing	28%	- Spark SQL - ETL Pipelines - Structured Streaming - Data Transformation
Monitoring and Troubleshooting	16%	- Troubleshooting - Monitoring - Performance Optimization
Data Quality and Governance	12%	- Data Quality - Data Lineage - Governance
Data Modeling and Storage	20%	- File Formats - Storage Optimization - Data Modeling
Databricks Lakehouse Platform	24%	- Data Management - Lakehouse Architecture - Unity Catalog - Delta Lake

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Databricks-Certified-Data-Engineer-Professional Vorbereitung & Databricks-Certified-Data-Engineer-Professional Lernhilfe

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Databricks Certified Data Engineer Professional Exam Databricks-Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q249-Q254):

249. Frage
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
B. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
C. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
D. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations

Antwort: D

Begründung:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade- off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job.

250. Frage
A data engineer wants to automate job monitoring and recovery in Databricks using the Jobs API.
They need to list all jobs, identify a failed job, and rerun it. Which sequence of API actions should the data engineer perform?

A. Use the jobs/cancel endpoint to remove failed jobs, then recreate them with jobs/create and run the new ones.
B. Use the jobs/get endpoint to retrieve job details, then use jobs/update to rerun failed jobs.
C. Use the jobs/list endpoint to list jobs, then use the jobs/create endpoint to create a new job, and run the new job using jobs/run-now.
D. Use the jobs/list endpoint to list jobs, check job run statuses with jobs/runs/list, and rerun a failed job using jobs/run-now.

Antwort: D

Begründung:
The Databricks Jobs REST API provides several endpoints for automation. The correct monitoring and rerun flow uses three specific calls:
GET /api/2.1/jobs/list - Lists all available jobs within the workspace.
GET /api/2.1/jobs/runs/list - Returns all runs for a specific job, including their current state (e.g., TERMINATED: FAILED).
POST /api/2.1/jobs/run-now - Immediately triggers a rerun of the specified job.
This sequence aligns with Databricks' prescribed automation model for job observability and recovery. Using jobs/update modifies metadata but does not rerun jobs, and jobs/create is only used for creating new jobs, not rerunning failed ones. Cancelling and recreating jobs introduces unnecessary duplication. Therefore, option A is the correct automated recovery workflow.

251. Frage
Which method can be used to determine the total wall-clock time it took to execute a query?

A. In the Spark UI, take the sum of all task durations that ran across all stages for all jobs associated with that query.
B. Open the Query Profiler associated with that query and use the Aggregated task time metric.
C. Open the Query Profiler associated with that query and use the Total wall-clock duration metric.
D. In the Spark UI, take the job duration of the longest-running job associated with that query.

Antwort: C

Begründung:
The Query Profiler in Databricks SQL and notebooks provides a detailed breakdown of query performance metrics. The "Total wall-clock duration" metric directly represents the total elapsed time from query start to completion, including all execution, planning, and waiting stages. In contrast, "Aggregated task time" reflects the cumulative duration across all parallel tasks, which does not equal the total elapsed wall time since tasks often run concurrently. Using job duration from Spark UI can underestimate or overestimate runtime when queries span multiple jobs.
Therefore, the Query Profiler's total wall-clock duration is the officially documented method to determine actual query execution time.

252. Frage
A data engineer has a Delta table orders with deletion vectors enabled. The engineer executes the following command:
DELETE FROM orders WHERE status = 'cancelled';
What should be the behavior of deletion vectors when the command is executed?

A. Rows are marked as deleted in metadata, not in files.
B. Files are physically rewritten without the deleted rows.
C. Rows are marked as deleted both in metadata and in files.
D. Delta automatically removes all cancelled orders permanently.

Antwort: A

Begründung:
Deletion vectors (DVs) in Delta Lake optimize delete operations by marking deleted rows logically in metadata rather than rewriting Parquet files. When a DELETE statement is executed, affected rows are tracked by DVs in the transaction log. The data remains in the underlying files but is filtered out during query reads. This improves performance for frequent deletes and updates since file rewrites are deferred. Physical data removal only occurs when a VACUUM command is later executed. The Databricks documentation confirms: "With deletion vectors, deleted rows are marked in metadata and skipped at read time, avoiding file rewrites." Thus, rows are marked as deleted in metadata--not in files.

253. Frage
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. Use Hive-style partitioning, as it provides efficient data skipping and is easy to change partition columns at any time.
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. Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.

Antwort: B

Begründung:
Liquid clustering is designed for managed tables under 1TB with evolving query patterns. It efficiently addresses data skew, continuously optimizes data layout, and allows clustering keys to be changed without requiring full data rewrites, making it well suited for frequently changing filter columns while minimizing maintenance overhead.

254. Frage
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