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DATABRICKS

Databricks Certified Data Engineer Professional

DATA-ENGINEER-PROFESSIONALData Engineer Professional

The Databricks Certified Data Engineer Professional certification validates your advanced skills in building, optimizing, and maintaining production-grade data engineering solutions on the Databricks Data Intelligence Platform. It is designed for experienced data engineers who architect secure, reliable, and cost-effective ETL pipelines, implement streaming workloads, and orchestrate workflows using Python and SQL. Earning this credential demonstrates your ability to deliver complex data engineering projects at scale.

Practice Free Questions

639 practice questions · Updated 2026-07-30

10Domains
26Objectives
199Concepts
639Questions

DATA-ENGINEER-PROFESSIONAL Curriculum

Every domain, objective, and concept the DATA-ENGINEER-PROFESSIONAL exam measures.

Using Python and Tools for development

7 concepts · 23 questions
  1. Scalable Python Project Structure for Bundles
  2. Bundle Configuration and Deployment Automation
  3. Managing PyPI Packages in Databricks
  4. Installing Local Wheels and Source Archives
  5. Troubleshooting Dependency Conflicts
  6. Pandas UDF Fundamentals
  7. Python UDF Development

Building and Testing an ETL pipeline with Lakeflow Spark Declarative Pipelines, SQL, and Apache Spark on the Databricks Platform

11 concepts · 29 questions
  1. Lakeflow Spark Declarative Pipelines fundamentals
  2. Autoloader for incremental data ingestion
  3. Managing pipeline lifecycle
  4. Automating ETL with Jobs
  5. Streaming tables vs materialized views
  6. AUTO CDC APIs for change data capture
  7. Choosing between Structured Streaming and Lakeflow Pipelines
  8. Control flow operators in pipelines
  9. Pipeline configuration for environments and dependencies
  10. Unit and integration testing for pipelines
  11. Using the built-in debugger for pipeline code

Design and implement data ingestion pipelines to efficiently ingest a variety of data formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, Text and Binary from diverse sources such as message buses and cloud storage.

15 concepts · 34 questions
  1. Data Format Characteristics
  2. Format Selection Criteria
  3. Delta Lake Ingestion
  4. Parquet and ORC Ingestion
  5. Avro Ingestion
  6. JSON Ingestion
  7. CSV Ingestion
  8. XML Ingestion
  9. Text Ingestion
  10. Binary Ingestion
  11. Message Bus Ingestion
  12. Cloud Storage Ingestion
  13. Incremental Ingestion Patterns
  14. Schema and Data Validation
  15. Performance Optimization for Ingestion

Create an append-only data pipeline capable of handling both batch and streaming data using Delta.

8 concepts · 24 questions
  1. Delta append-only pipelines
  2. Batch and streaming ingestion
  3. Structured Streaming with Delta
  4. Schema enforcement and evolution
  5. Idempotent writes
  6. Delta table properties for append-only
  7. Handling late and out-of-order data
  8. Monitoring and checkpointing

Write efficient Spark SQL and PySpark code to apply advanced data transformations, including window functions, joins, and aggregations, to manipulate and analyze large Datasets.

6 concepts · 20 questions
  1. Window Functions
  2. Advanced Joins
  3. Aggregations
  4. Optimizing Transformations
  5. PySpark DataFrame API
  6. Spark SQL Syntax

Develop a quarantining process for bad data with Lakeflow Spark Declarative Pipelines, or autoloader in classic jobs.

5 concepts · 19 questions
  1. Quarantine bad data in Lakeflow Spark Declarative Pipelines
  2. Quarantine bad data with Autoloader in classic jobs
  3. Define bad data criteria for quarantine
  4. Implement quarantine workflow with error handling
  5. Monitor and manage quarantined data

Demonstrate delta sharing securely between Databricks deployments using Databricks to Databricks Sharing (D2D) or to external platforms using the open sharing protocol (D2O).

7 concepts · 23 questions
  1. Delta Sharing Architecture
  2. Databricks-to-Databricks Sharing (D2D)
  3. Open Sharing Protocol (D2O)
  4. Security and Access Control
  5. Sharing Data Assets
  6. Recipient Management
  7. Troubleshooting and Monitoring

Configure Lakehouse Federation with proper governance across the supported source Systems.

9 concepts · 25 questions
  1. Lakehouse Federation Overview
  2. Supported Source Systems
  3. Creating Foreign Catalogs
  4. Managing Foreign Connections
  5. Querying Federated Data
  6. Governance with Unity Catalog
  7. Security and Credential Management
  8. Performance Considerations
  9. Troubleshooting and Monitoring

Use Delta Share to share live data from Lakehouse to any computing platform.

7 concepts · 29 questions
  1. Delta Sharing Overview
  2. Delta Sharing Architecture
  3. Creating a Delta Share
  4. Managing Recipients
  5. Sharing Live Data
  6. Accessing Shared Data
  7. Security and Permissions

Monitoring

8 concepts · 27 questions
  1. System Tables Overview
  2. Querying System Tables
  3. Interpreting System Table Data
  4. Query Profiler UI Usage
  5. Spark UI Usage
  6. Databricks REST API for Monitoring
  7. Databricks CLI for Monitoring
  8. Lakeflow Spark Declarative Pipelines Event Logs

Alerting

6 concepts · 16 questions
  1. SQL Alert Creation
  2. SQL Alert Management
  3. SQL Alert Notifications
  4. Job Status Notifications via UI
  5. Job Performance Notifications via UI
  6. Job Notifications via Jobs API

Understand how / why using Unity Catalog managed tables reduces operations overhead and maintenance burden.

8 concepts · 25 questions
  1. Managed vs External Tables
  2. Automatic Lifecycle Management
  3. Centralized Governance
  4. No Manual Location Management
  5. Built-in Data Lineage
  6. Simplified Schema Evolution
  7. Reduced Storage Cleanup Effort
  8. Consistent Metadata Handling

Understand delta optimization techniques, such as deletion vectors and liquid clustering.

9 concepts · 29 questions
  1. Deletion Vectors Overview
  2. Deletion Vectors Mechanics
  3. Deletion Vectors Benefits and Trade-offs
  4. Deletion Vectors Configuration and Usage
  5. Liquid Clustering Overview
  6. Liquid Clustering Mechanics
  7. Liquid Clustering Benefits and Trade-offs
  8. Liquid Clustering Configuration and Usage
  9. Comparison and Selection

Understand the optimization techniques used by Databricks to ensure the performance of queries on large datasets (data skipping, file pruning, etc.).

8 concepts · 30 questions
  1. Data Skipping
  2. File Pruning
  3. Z-Ordering
  4. Partitioning vs. Z-Ordering
  5. Statistics Collection
  6. Optimized Writes
  7. Auto Optimize
  8. Bloom Filter Indexing

Apply Change Data Feed (CDF) to address specific limitations of streaming tables and enhance latency.

5 concepts · 15 questions
  1. CDF limitations in streaming tables
  2. CDF for latency enhancement
  3. CDF configuration in Delta Lake
  4. CDF read options for streaming
  5. CDF vs. streaming table trade-offs

Use the query profile to analyze the query and identify bottlenecks, such as bad data skipping, inefficient types of joins, and data shuffling.

5 concepts · 17 questions
  1. Query Profile Overview
  2. Identifying Bottlenecks
  3. Data Skipping Analysis
  4. Join Type Efficiency
  5. Data Shuffling Detection

Applying Data Security mechanisms.

8 concepts · 23 questions
  1. ACL Basics
  2. Workspace Object Permissions
  3. Principle of Least Privilege
  4. Policy Enforcement with ACLs
  5. Row Filters
  6. Column Masks
  7. Anonymization Techniques
  8. Pseudonymization Techniques

Ensuring Compliance

8 concepts · 28 questions
  1. PII Detection in Pipelines
  2. Dynamic Masking Techniques
  3. Streaming Masking Implementation
  4. Batch Masking Implementation
  5. Compliance-Driven Pipeline Design
  6. Data Retention Policy Interpretation
  7. Data Purging Implementation
  8. Purge Verification and Auditing

Create and add descriptions/metadata about enterprise data to make it more discoverable.

7 concepts · 30 questions
  1. Understanding Databricks Unity Catalog metadata
  2. Adding table and column comments
  3. Setting table properties
  4. Managing schema descriptions
  5. Using the Catalog Explorer for metadata
  6. Querying metadata with DESCRIBE and SHOW
  7. Documenting data lineage and ownership

Demonstrate understanding of Unity Catalog permission inheritance model.

9 concepts · 18 questions
  1. Unity Catalog permission inheritance hierarchy
  2. Inheritance of privileges from parent to child objects
  3. Overriding inherited permissions
  4. Effect of deny rules on inheritance
  5. Permission inheritance for tables and views
  6. Permission inheritance for functions and models
  7. Permission inheritance for volumes and external locations
  8. Inheritance of ownership
  9. Best practices for managing permission inheritance

Debugging and Troubleshooting

9 concepts · 34 questions
  1. Spark UI navigation for diagnostics
  2. Cluster log analysis
  3. System table queries for job metadata
  4. Query profile interpretation
  5. Error analysis and remediation
  6. Job repairs for failed runs
  7. Parameter overrides for job runs
  8. Lakeflow Spark Declarative Pipelines event logs
  9. Spark UI for pipeline debugging

Deploying CI/CD

7 concepts · 25 questions
  1. Declarative Automation Bundles basics
  2. Bundle configuration files
  3. Deploying resources with bundles
  4. Bundle lifecycle and validation
  5. Git-based CI/CD integration
  6. CI/CD workflow configuration
  7. Automated deployment in CI/CD

Design and implement scalable data models using Delta Lake to manage large datasets.

7 concepts · 29 questions
  1. Delta Lake table design for large datasets
  2. Delta Lake schema evolution and enforcement
  3. Delta Lake time travel and versioning
  4. Delta Lake optimization techniques
  5. Delta Lake data skipping and statistics
  6. Delta Lake transaction and concurrency control
  7. Delta Lake table maintenance and lifecycle

Simplify data layout decisions and optimize query performance using Liquid Clustering.

7 concepts · 20 questions
  1. Liquid Clustering Overview
  2. Clustering Keys
  3. Automatic Layout Management
  4. Query Performance Optimization
  5. Table Creation with Liquid Clustering
  6. Altering Clustering Keys
  7. Compatibility and Limitations

Identify the benefits of using liquid Clustering over Partitioning and ZOrder.

6 concepts · 23 questions
  1. Liquid Clustering Fundamentals
  2. Partitioning Limitations
  3. ZOrder Limitations
  4. Benefits of Liquid Clustering
  5. Comparison with Partitioning and ZOrder
  6. Use Cases for Liquid Clustering

Design Dimensional Models for analytical workloads, ensuring efficient querying and aggregation.

7 concepts · 24 questions
  1. Dimensional Modeling Fundamentals
  2. Star Schema Design
  3. Snowflake Schema Design
  4. Fact Table Types
  5. Dimension Table Types
  6. Granularity and Aggregation
  7. Query Optimization in Dimensional Models
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Practice Free Questions

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