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Instant Download Databricks : Databricks-Certified-Data-Engineer-Professional Questions & Answers as PDF & Test Engine
- Exam Code: Databricks-Certified-Data-Engineer-Professional
- Exam Name: Databricks Certified Data Engineer Professional Exam
- Updated: Oct 11, 2026
- No. of Questions: 250 Questions and Answers
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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Modelling | 6% | - Optimize table design and partitioning - Implement dimensional and relational models - Design Medallion Architecture |
| Data Governance | 7% | - Enforce data policies and standards - Use Unity Catalog for governance - Manage data assets and metadata |
| Cost & Performance Optimisation | 13% | - Apply cost management best practices - Optimize compute and storage resources - Improve query and pipeline performance |
| Monitoring and Alerting | 10% | - Track data lineage and metrics - Set up alerts and notifications - Monitor pipeline performance and health |
| Ensuring Data Security and Compliance | 10% | - Implement access control and permissions - Ensure data privacy and compliance - Secure data at rest and in transit |
| Data Ingestion & Acquisition | 7% | - Use Auto Loader and structured streaming - Ingest data from diverse sources - Handle incremental and batch data loads |
| Data Sharing and Federation | 5% | - Implement Lakehouse Federation - Manage cross-platform data access - Use Delta Sharing for secure data sharing |
| Data Transformation, Cleansing, and Quality | 10% | - Enforce data quality standards - Apply data cleansing and validation rules - Implement schema evolution and management |
| Debugging and Deploying | 10% | - Implement CI/CD and DevOps practices - Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs |
| Developing Code for Data Processing using Python and SQL | 22% | - Use Databricks-specific libraries and APIs - Write efficient and maintainable code - Implement complex data processing logic |
Databricks Certified Data Engineer Professional Sample Questions:
A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?
- A. Date
- B. latitude
- C. Post_time
- D. User_id
- E. Post_id
Correct Answer: A 🗳️
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A data engineer manages a Unity Catalog table customer_data in schema finance that includes sensitive fields like ssn and credit_score. Intern Group should only see masked values, while Analyst Group should only access rows for their assigned region. The data engineer needs to restrict access based on user role and region without duplicating data. How should the data engineer enforce this security policy?
- A. Create views using current_user() and is_account_group_member() functions, and apply masking logic inside the SQL SELECT clause for each sensitive column.
- B. Create dynamic views for each user role and manage access with ACLs.
- C. Use Unity Catalog's row filters based on the user roles and column masks based on the region.
- D. Use Unity Catalog's row filters based on the region and column masks based on user roles.
Correct Answer: D 🗳️
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A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis. They have a date dimension table and are evaluating design options to ensure it supports a wide range of time-based analyses. Which design approach for the date dimension will support efficient time-based querying and aggregation?
- A. Create separate dimension tables for different calendar systems (fiscal, academic, etc.)
- B. Store the date as string in ISO format (YYYY-MM-DD) for readability.
- C. Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.
- D. Store only the date value and calculate all time attributes in queries.
Correct Answer: C 🗳️
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A platform engineer is creating catalogs and schemas for the development team to use.
The engineer has created an initial catalog, catalog_A, and initial schema, schema_A. The engineer has also granted USE CATALOG, USE SCHEMA, and CREATE TABLE to the development team so that the engineer can begin populating the schema with new tables.
Despite being owner of the catalog and schema, the engineer noticed that they do not have access to the underlying tables in Schema_A.
What explains the engineer's lack of access to the underlying tables?
- A. The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
- B. Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.
- C. The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at any point.
- D. Users granted with USE CATALOG can modify the owner's permissions to downstream tables.
Correct Answer: C 🗳️
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A job runs four independent tasks (X, Y, Z, W) in parallel to process regional sales data. The Data Engineering team recently updated its cluster policy to ban cost-prohibitive instance types. Task Y now fails due to the newly enforced cluster policy restricting the use of a specific instance type.
A data engineer needs to resolve the failure quickly without disrupting the other tasks. How should the data engineer resolve the failure of tasks?
- A. Manually create a new cluster for Task Y, update the job configuration, and trigger a full re-run.
- B. Delete the failed run, disable the cluster policy, and re-execute all tasks.
- C. Use "Repair run", override the cluster configuration for Task Y to use a permitted instance type, and let Databricks re-run only Task Y.
- D. Edit the global cluster policy to allow the restricted instance type, then re-run the entire job.
Correct Answer: C 🗳️
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