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

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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic. What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A. Can edit
B. Can Read
C. Can manage
D. Can run


Question 2

A data engineering team needs to implement a tagging system for their tables as part of an automated ETL process, and needs to apply tags programmatically to tables in Unity Catalog.
Which SQL command adds tags to a table programmatically?

A. COMMENT ON TABLE table_name TAGS ('key1' = 'value1', 'key2' = 'value2');
B. ALTER TABLE table_name SET TAGS ('key1' = 'value1', 'key2' = 'value2');
C. SET TAGS FOR table_name AS ('key1' = 'value1', 'key2' = 'value2');
D. APPLY TAGS ON table_name VALUES ('key1' = 'value1', 'key2' = 'value2');


Question 3

The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A. preds.write.format("delta").save("/preds/churn_preds")
B.

C.

D.

E. preds.write.mode("append").saveAsTable("churn_preds")


Question 4

The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

A. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
B. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
C. The source query failed to update properly for three consecutive minutes and then restarted
D. The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
E. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query


Question 5

A data engineer is designing an append-only pipeline that needs to handle both batch and streaming data in Delta Lake. The team wants to ensure that the streaming component can efficiently track which data has already been processed. Which configuration should be set to enable this?

A. overwriteSchema
B. checkpointLocation
C. mergeSchema
D. partitionBy


Solutions:

Question 1
Answer: B
Question 2
Answer: B
Question 3
Answer: E
Question 4
Answer: A
Question 5
Answer: B

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