Databricks Databricks-Certified-Data-Engineer-Professional Q&A - in .pdf

  • Databricks-Certified-Data-Engineer-Professional pdf
  • Exam Code: Databricks-Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional Exam
  • Updated: Sep 11, 2026
  • Q & A: 250 Questions and Answers
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  • Exam Name: Databricks Certified Data Engineer Professional Exam
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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

SectionWeightObjectives
Data Transformation, Cleansing, and Quality10%- Apply data cleansing and validation rules
- Implement schema evolution and management
- Enforce data quality standards
Data Sharing and Federation5%- Use Delta Sharing for secure data sharing
- Implement Lakehouse Federation
- Manage cross-platform data access
Cost & Performance Optimisation13%- Optimize compute and storage resources
- Improve query and pipeline performance
- Apply cost management best practices
Monitoring and Alerting10%- Set up alerts and notifications
- Track data lineage and metrics
- Monitor pipeline performance and health
Developing Code for Data Processing using Python and SQL22%- Write efficient and maintainable code
- Implement complex data processing logic
- Use Databricks-specific libraries and APIs
Debugging and Deploying10%- Implement CI/CD and DevOps practices
- Troubleshoot and debug pipelines
- Deploy using Asset Bundles, CLI, and APIs
Data Ingestion & Acquisition7%- Use Auto Loader and structured streaming
- Ingest data from diverse sources
- Handle incremental and batch data loads
Data Governance7%- Use Unity Catalog for governance
- Enforce data policies and standards
- Manage data assets and metadata
Ensuring Data Security and Compliance10%- Ensure data privacy and compliance
- Implement access control and permissions
- Secure data at rest and in transit
Data Modelling6%- Optimize table design and partitioning
- Implement dimensional and relational models
- Design Medallion Architecture

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion.
However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

A. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
B. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
C. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
D. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
E. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.


Question 2

A data engineer wants to enforce the principle of least privilege when configuring ACLs for Databricks jobs in a collaborative workspace. Which approach should the data engineer use?

A. Assign users only the minimum permission level (e.g., CAN RUN or CAN VIEW) required for their role on each job.
B. Grant all users CAN MANAGE permission on all jobs to avoid access issues.
C. Grant CAN RUN permission to everyone and CAN MANAGE to a single admin group.
D. Use only folder-level permissions and avoid setting permissions on individual jobs.


Question 3

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. "Manage" permission should be set on a secret scope containing only 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. "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.


Question 4

A data engineer is using Lakeflow Declarative Pipeline to propagate row deletions from a source bronze table (user_bronze) to a target silver table (user_silver). The engineer wants deletions in user_bronze to automatically delete corresponding rows in user_silver during pipeline execution.
Which configuration ensures deletions in the bronze table are propagated to the silver table?

A. Enable Change Data Feed (CDF) on user_bronze, read its CDF stream, and use apply_changes() with apply_as_deletes=True for user_silver.
B. Configure VACUUM on user_bronze to delete files, then rebuild user_silver from scratch.
C. Use apply_changes without CDF and filter rows where _soft_deleted is true.
D. Enable CDF on user_silver, read its transaction log, and use MERGE to sync deletions.


Question 5

The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity.
Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

A. Cluster creation allowed. "Can Attach To" privileges on the required cluster
B. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster
C. Cluster creation allowed. "Can Restart" privileges on the required cluster
D. "Can Manage" privileges on the required cluster
E. "Can Restart" privileges on the required cluster


Solutions:

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

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