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

Section	Weight	Objectives
Security and Governance	~10%	- Implement row-level security, column masking, and compliance - Manage Unity Catalog permissions and ACLs
Monitoring, Logging, and Troubleshooting	~8%	- Use Spark UI, Query Profiler, and system tables - Diagnose common pipeline and job failures
Developing Code for Data Processing using Python and SQL	~22%	- Implement scalable Python/SQL code and project structures - Manage dependencies, libraries, and UDFs - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader
Streaming Workloads and Change Data Capture	~11%	- Implement reliable streaming pipelines - Apply AUTO CDC APIs and exactly-once semantics
Cost and Performance Optimization	~13%	- Optimize queries, clusters, and storage - Leverage system tables and observability tools
Data Transformation, Cleansing, and Quality	~12%	- Apply advanced Spark transformations - Enforce data quality and quarantine bad data
Data Sharing and Federation	~8%	- Configure Delta Sharing and Lakehouse Federation
CI/CD, Testing, and Deployment	~6%	- Implement testing and deployment pipelines - Deploy with Declarative Automation Bundles, CLI, and REST API
Data Modeling	~10%	- Design scalable Delta Lake schemas and clustering - Apply dimensional modeling techniques

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Databricks Certified Data Engineer Professional Sample Questions (Q39-Q44):

NEW QUESTION # 39
The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible.
A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production data. Because all users have Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team.
Which statement captures best practices for this situation?

A. Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.
B. All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.
C. In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.
D. Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.

Answer: C

Explanation:
The best practice in such scenarios is to ensure that production data is handled securely and with proper access controls. By granting only read access to production data in development and testing environments, it mitigates the risk of unintended data modification. Additionally, maintaining isolated databases for different environments helps to avoid accidental impacts on production data and systems.

NEW QUESTION # 40
A data engineer deploys a multi-task Databricks job that orchestrates three notebooks. One task intermittently fails with Exit Code 1 but succeeds on retry. The engineer needs to collect detailed logs for the failing attempts, including stdout/stderr and cluster lifecycle context, and share them with the platform team. What steps the data engineer needs to follow using built-in tools?

A. Export the notebook run results to HTML; this bundle includes complete stdout, stderr, and cluster event history across all tasks.
B. Download worker logs directly from the Spark UI and ignore driver logs, as worker logs contain stdout/stderr for all tasks and cluster events.
C. From the job run details page, export the job's logs or configure log delivery; then retrieve the compute driver logs and event logs from the compute details page to correlate stdout/stderr with cluster events.
D. Use the notebook interactive debugger to re-run the entire multi-task job, and capture step- through traces for the failing task.

Answer: C

Explanation:
The recommended way to troubleshoot and collect detailed job logs is through the Job Run Details page in Databricks. From there, engineers can export run logs or configure automatic log delivery to a storage destination. The driver and event logs available under compute details provide stdout, stderr, and cluster lifecycle context required for root-cause analysis.

NEW QUESTION # 41
A data engineer is brining an existing production Databricks job under asset bundle management and wants to ensure that:
- The job's current configuration is captured as YAML, and all
referenced files are included in their bundle project.
- Future changes to the bundle's YAML will update the existing job in-
place (not create a new job)
How should the data engineer successfully move the production job under asset bundle management?

A. Run Databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deploy to deploy the bundle, which will always update the existing job automatically.
B. Manually create the YAML configuration for the job in your bundle project, ensuring all settings match the existing job. Then, run Databricks bundle deploy the bundle, which will update the existing job in your workspace.
C. Run databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deployment, bind to link the bundle's job resource to the existing job in Databricks.
D. Export the job definition as JSON, convert it to YAML, and place it in your bundle. Then, run Databricks bundle deploy to update the existing job.

Answer: C

Explanation:
Generating the bundle from the existing job captures the full job configuration as YAML and pulls in all referenced files into the bundle project. Binding the generated job resource to the existing Databricks job establishes a persistent link, ensuring that future bundle deployments update the same production job in place rather than creating a new one.

NEW QUESTION # 42
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A. lag("event_time", "10 minutes").alias("time")
B. to_interval("event_time", "5 minutes").alias("time")
C. "event_time"
D. window("event_time", "10 minutes").alias("time")
E. window("event_time", "5 minutes").alias("time")

Answer: E

Explanation:
This is the correct answer because the window function is used to group streaming data by time intervals. The window function takes two arguments: a time column and a window duration. The window duration specifies how long each window is, and must be a multiple of 1 second. In this case, the window duration is "5 minutes", which means each window will cover a non-overlapping five- minute interval. The window function also returns a struct column with two fields: start and end, which represent the start and end time of each window. The alias function is used to rename the struct column as "time".

NEW QUESTION # 43
A data engineer is troubleshooting a slow-running Delta Lake query on Databricks SQL involves complex joins and large datasets. They need to identify whether the root cause is related to poor data skipping, inefficient join strategies, or excessive data shuffling. Which approach should identify the specific bottlenecks using native Databricks tools?

A. Check the query's execution time in the Jobs UI and correlate it with cluster resource utilization metrics.
B. Use the LIMIT clause to run a subset of the query and compare execution times with the full dataset.
C. Analyze the Top Operators panel in the Query Profile to identify high-cost operations like BroadcastNestedLoopJoin
D. Enable the EXPLAIN command to review the parsed logical plan and manually estimate shuffle sizes.

Answer: C

Explanation:
The Query Profile's Top Operators panel surfaces the most expensive operators in the query execution, making it possible to directly identify bottlenecks such as inefficient join strategies, poor data skipping, or excessive shuffling. This native visualization highlights where time and resources are spent, enabling precise root-cause analysis for slow-running queries.

NEW QUESTION # 44
......

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