- Exam Code: Databricks-Certified-Data-Engineer-Professional
- Exam Name: Databricks Certified Data Engineer Professional Exam
- Updated: Sep 23, 2026
- Q & A: 250 Questions and Answers
Databricks Certified Data Engineer Professional : Databricks-Certified-Data-Engineer-Professional exam
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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitoring and Alerting | 10% | - Set up alerts and notifications - Track data lineage and metrics - Monitor pipeline performance and health |
| Topic 2: Cost & Performance Optimisation | 13% | - Apply cost management best practices - Optimize compute and storage resources - Improve query and pipeline performance |
| Topic 3: Ensuring Data Security and Compliance | 10% | - Implement access control and permissions - Secure data at rest and in transit - Ensure data privacy and compliance |
| Topic 4: Data Governance | 7% | - Enforce data policies and standards - Manage data assets and metadata - Use Unity Catalog for governance |
| Topic 5: Debugging and Deploying | 10% | - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices |
| Topic 6: Data Modelling | 6% | - Implement dimensional and relational models - Optimize table design and partitioning - Design Medallion Architecture |
| Topic 7: Data Sharing and Federation | 5% | - Use Delta Sharing for secure data sharing - Implement Lakehouse Federation - Manage cross-platform data access |
| Topic 8: Data Transformation, Cleansing, and Quality | 10% | - Apply data cleansing and validation rules - Implement schema evolution and management - Enforce data quality standards |
| Topic 9: Developing Code for Data Processing using Python and SQL | 22% | - Implement complex data processing logic - Write efficient and maintainable code - Use Databricks-specific libraries and APIs |
| Topic 10: Data Ingestion & Acquisition | 7% | - Use Auto Loader and structured streaming - Ingest data from diverse sources - Handle incremental and batch data loads |
Databricks Certified Data Engineer Professional Sample Questions:
Question #1
A company stores account transactions in a Delta Lake table. The company needs to apply frequent account-level correlations (e.g., UPDATE statements) but wants to avoid rewriting entire Parquet files for each change to reduce file churn and improve write performance. Which Delta Lake feature should they enable?
- A. Partition the Delta table by account_id
- B. Enable automatic file compaction on writes
- C. Enable change data feed on the Delta table
- D. Enable deletion vectors on the Delta table
Correct Answer: D π³οΈ
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Question #2
The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?
- A. "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.
- B. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
- C. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
- D. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
Correct Answer: B π³οΈ
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Question #3
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?
- A. workspace
- B. fs
- C. jobs
- D. configure
- E. libraries
Correct Answer: B π³οΈ
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Question #4
Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?
- A. spark.sql.files.maxPartitionBytes
- B. spark.sql.files.openCostInBytes
- C. spark.sql.adaptive.coalescePartitions.minPartitionNum
- D. spark.sql.autoBroadcastJoinThreshold
- E. spark.sql.adaptive.advisoryPartitionSizeInBytes
Correct Answer: A π³οΈ
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Question #5
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?
- A. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
- B. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
- C. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.
- D. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
- E. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
Correct Answer: E π³οΈ
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