Databricks Certified-Data-Engineer-Professional Questions & Answers - in .pdf
- Total Q&A: 250
- Update: Oct 11, 2026
- Price: $59.99
- Vendor: Databricks
- Exam Code: Certified-Data-Engineer-Professional
- Exam Name: Databricks Certified Data Engineer Professional
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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|
| Data Modelling | - Dimensional Modelling
- 1. Design dimensional models for analytical workloads
- Scalable Data Models
- 1. Design and implement scalable data models using Delta Lake
- 2. Optimize data layout using Liquid Clustering
- 3. Understand Liquid Clustering versus partitioning and Z-Ordering
|
| Data Sharing and Federation | - Lakehouse Federation
- 1. Configure Lakehouse Federation with appropriate governance
- Delta Sharing
- 1. Share live Lakehouse data with external computing platforms
- 2. Configure sharing with external platforms using the open sharing protocol
- 3. Configure Databricks-to-Databricks Sharing
|
| Monitoring and Alerting | - Alerting
- 1. Use SQL Alerts for data quality monitoring
- 2. Configure Lakeflow Jobs notifications for job status and performance issues
- Monitoring
- 1. Use Lakeflow Spark Declarative Pipelines event logs for monitoring
- 2. Use Query Profiler and Spark UI to monitor workloads
- 3. Use Databricks REST APIs and CLI for monitoring jobs and pipelines
- 4. Use system tables for resource, cost, audit, and workload monitoring
|
| Debugging and Deploying | - Deploying CI/CD
- 1. Integrate Git-based CI/CD workflows using Databricks Git Folders
- 2. Build and deploy Databricks resources using Databricks Asset Bundles
- Debugging and Troubleshooting
- 1. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging
- 2. Analyze errors and remediate failed job runs
- 3. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics
|
| Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
- 1. Ingest data from message buses and cloud storage
- 2. Build append-only pipelines for batch and streaming data using Delta
- 3. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data
|
| Data Governance | - Unity Catalog Permissions
- 1. Understand the Unity Catalog permission inheritance model
- Metadata and Discoverability
- 1. Create and maintain descriptions and metadata for enterprise data
|
| Cost & Performance Optimisation | - Delta Optimization
- 1. Understand deletion vectors and liquid clustering
- 2. Use Change Data Feed to address streaming table limitations and improve latency
- 3. Apply data skipping and file pruning techniques
- Query Performance
- 1. Identify inefficient joins and excessive data shuffling
- 2. Use Query Profile to identify performance bottlenecks
- Cost Optimization
- 1. Understand how Unity Catalog managed tables reduce operational overhead
|
| Developing Code for Data Processing using Python and SQL | - Building and Testing ETL Pipelines
- 1. Develop unit and integration tests for data processing code
- 2. Configure environments, dependencies, memory, and retry behavior
- 3. Use control flow operators in pipeline components
- 4. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines
- 5. Create and automate ETL workloads using Jobs through UI, APIs, and CLI
- 6. Compare streaming tables and materialized views
- 7. Use APPLY CHANGES APIs for change data capture
- 8. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader
- Using Python and Tools for Development
- 1. Develop User-Defined Functions using Pandas/Python UDFs
- 2. Manage and troubleshoot third-party library installations and dependencies
- 3. Design and implement scalable Python project structures optimized for Databricks Asset Bundles
|
| Ensuring Data Security and Compliance | - Data Security
- 1. Use ACLs to secure workspace objects and enforce least privilege
- 2. Apply anonymization and pseudonymization techniques
- 3. Use row filters and column masks for sensitive data
- Compliance
- 1. Develop data purging solutions according to data retention policies
- 2. Implement pipelines that detect and mask personally identifiable information
|
| Data Transformation, Cleansing, and Quality | - Data Quality
- 1. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader
- 2. Develop data quarantining processes for invalid data
- Advanced Data Transformation
- 1. Write efficient Spark SQL and PySpark transformations
- 2. Apply window functions, joins, and aggregations to large datasets
|
Databricks Certified Data Engineer Professional Sample Questions:
Question #1
A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of information is critical to this decision?
A. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
B. Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.
C. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
D. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.
E. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
Question #2
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. Enable deletion vectors on the Delta table
B. Partition the Delta table by account_id
C. Enable change data feed on the Delta table
D. Enable automatic file compaction on writes
Question #3
A data engineer has a Delta table orders with deletion vectors enabled. The engineer executes the following command:
DELETE FROM orders WHERE status = 'cancelled';
What should be the behavior of deletion vectors when the command is executed?
A. Delta automatically removes all cancelled orders permanently.
B. Rows are marked as deleted both in metadata and in files.
C. Files are physically rewritten without the deleted rows.
D. Rows are marked as deleted in metadata, not in files.
Question #4
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?
A. Total VMs: 8
50 GB per Executor
20 Cores / Executor
B. Total VMs: 1
400 GB per Executor
160 Cores / Executor
C. Total VMs: 4
100 GB per Executor
40 Cores/Executor
D. Total VMs: 2
200 GB per Executor
80 Cores / Executor
Question #5
While reviewing a query's execution in the Databricks Query Profiler, a data engineer observes that the Top Operators panel shows a Sort operator with high Time Spent and Memory Peak metrics. The Spark UI also reports frequent data spilling. How should the data engineer address this issue?
A. Increase the number of shuffle partitions to better distribute data.
B. Switch to a broadcast join to reduce memory usage.
C. Convert the sort operation to a filter operation.
D. Repartition the DataFrame to a single partition before sorting.
Solutions:
Question #1
Correct Answer: C | Question #2
Correct Answer: A | Question #3
Correct Answer: D | Question #4
Correct Answer: B | Question #5
Correct Answer: A |
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