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

Section	Objectives
Data Modeling and Transformation	- Spark SQL transformations - Dimensional modeling concepts - Performance optimization techniques
Delta Lake and Data Management	- Delta Lake transactions and ACID properties - Time travel and versioning - Schema evolution and enforcement
Data Ingestion and Processing	- Batch and streaming ingestion with Auto Loader - ETL pipeline design patterns - Structured Streaming fundamentals
Production Pipelines and Orchestration	- Error handling and recovery strategies - Job scheduling and monitoring - Databricks Workflows
Databricks Lakehouse Platform Architecture	- Data governance concepts (Unity Catalog basics) - Workspace and cluster architecture - Medallion architecture (Bronze, Silver, Gold)

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q21-Q26):

NEW QUESTION # 21
What statement is true regarding the retention of job run history?

A. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
B. It is retained until you export or delete job run logs
C. It is retained for 90 days or until the run-id is re-used through custom run configuration
D. It is retained for 60 days, after which logs are archived
E. It is retained for 60 days, during which you can export notebook run results to HTML

Answer: E

Explanation:
https://docs.databricks.com/en/workflows/jobs/monitor-job-runs.html

NEW QUESTION # 22
A data engineering team is migrating off its legacy Hadoop platform. As part of the process, they are evaluating storage formats for performance comparison. The legacy platform uses ORC and RCFile formats. After converting a subset of data to Delta Lake, they noticed significantly better query performance. Upon investigation, they discovered that queries reading from Delta tables leveraged a Shuffle Hash Join, whereas queries on legacy formats used Sort Merge Joins. The queries reading Delta Lake data also scanned less data. Which reason could be attributed to the difference in query performance?

A. The queries against the ORC tables leveraged the dynamic data skipping optimization but not the dynamic file pruning optimization.
B. The queries against the Delta Lake tables were able to leverage the dynamic file pruning optimization.
C. Delta Lake enables data skipping and file pruning using a vectorized Parquet reader.
D. Shuffle Hash Joins are always more efficient than Sort Merge Joins.

Answer: C

Explanation:
Delta Lake outperforms legacy Hadoop formats because it leverages Parquet-based storage, data skipping, and file pruning. According to Databricks documentation, Delta Lake automatically stores detailed statistics (min/max values and file-level metadata) in the transaction log. During query planning, the engine uses these statistics to skip entire files that do not match query filters, a process called data skipping and file pruning. Additionally, Delta uses a vectorized Parquet reader, which reduces I/O and CPU overhead. Together, these optimizations allow Delta to scan significantly less data and produce more efficient physical query plans (e.g., Shuffle Hash Join instead of Sort Merge Join). The performance gain is due to efficient data skipping, not the inherent superiority of join type.

NEW QUESTION # 23
A data engineer has created a new cluster using shared access mode with default configurations.
The data engineer needs to allow the development team access to view the driver logs if needed.
What are the minimal cluster permissions that allow the development team to accomplish this?

A. CAN VIEW
B. CAN RESTART
C. CAN ATTACH TO
D. CAN MANAGE

Answer: A

Explanation:
The CAN VIEW permission on a cluster allows users to see cluster details, including driver and executor logs. This is the minimal permission required for the development team to access logs without granting them the ability to modify, restart, or attach notebooks to the cluster.

NEW QUESTION # 24
A data engineer is creating a daily reporting job. There are two reporting notebooks--one for weekdays and one for weekends. An "if/else condition" task is configured as
{{job.start_time.is_weekday}} == true to route the job to either the weekday or weekend notebook tasks. The same job would be used across multiple time zones. Which action should a senior data engineer take upon reviewing the job to merge or reject the pull request?

A. Reject, as they should use {{job.trigger_time.is_weekday}} instead.
B. Reject, as the {{job.start_time.is_weekday}} is not a valid value reference.
C. Reject, as the {{job.start_time.is_weekday}} is for the UTC timezone.
D. Merge, as the job configuration looks good.

Answer: C

Explanation:
Databricks parameter templates like {{job.start_time.is_weekday}} evaluate in UTC time by default, not in local workspace or regional time zones. Therefore, when jobs are configured to run across different time zones, relying on is_weekday using UTC may cause scheduling and task routing mismatches (for example, triggering the weekday notebook in one region while it's still the weekend locally).
Databricks recommends adjusting conditional logic or pipeline parameters explicitly to handle time zone conversions if business requirements depend on local times. Because the engineer's configuration does not account for this behavior, a senior data engineer should reject the pull request and suggest time-zone-aware logic before merging.

NEW QUESTION # 25
A data engineer is implementing a job to download multiple PDF files from a third-party provided REST API endpoint by specifying different report types. The REST API is time-consuming and encounters intermittent errors, so the engineer wants to track each download activity to know when it fails and to retry partially, while providing scalable throughput. The engineer needs to download ten report types, and the list can be changed over time. How should the data engineer achieve this?

A. Use a Delta Lake table to track each report download status as 10 rows, and use it as a source table to execute the download function as a Pandas UDF.
B. Define a list variable within a Notebook to loop through the report types to download them, and print the download results. Execute it as a Notebook tasks.
C. Use a foreach task with a list of report types as its inputs.
D. Define ten Notebook tasks to clearly track which report download failed.

Answer: C

Explanation:
A foreach task allows the job to dynamically iterate over a configurable list of report types, execute downloads in parallel, and track the success or failure of each item independently. This enables scalable throughput, partial retries for failed downloads, and easy updates when the list of report types changes, without hardcoding tasks or introducing unnecessary complexity.

NEW QUESTION # 26
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