CERTIFIED-DATA-ENGINEER-PROFESSIONAL Real Exam Questions
Databricks Certified Data Engineer Professional. Everything you need to prepare, practice, and pass.
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Certification Overview
The exam heavily emphasizes Delta Lake as the core storage format, with deep coverage of data ingestion patterns, streaming with Spark Structured Streaming, and production-grade considerations like performance tuning and data governance. You'll need to understand how to evolve schemas safely, optimize lakehouse performance, manage access at scale, and implement time-travel for data recovery and audits.
What This Certification Proves
This certification validates expertise in building and maintaining data engineering solutions on the Databricks platform, specifically leveraging Delta Lake and Apache Spark. It demonstrates proficiency in data ingestion, streaming, performance optimization, and the governance features critical to enterprise data engineering. Passing this exam signals competency in modern lakehouse architecture and makes candidates immediately productive in Databricks-centric organizations.
Who Should Take This Exam
Intermediate data engineers with 2+ years of hands-on Spark or data pipeline experience looking to specialize in Databricks. Also suitable for cloud engineers transitioning into data engineering roles, or analysts leveling up to engineering. Requires solid SQL and Python/Scala foundation; not recommended for complete beginners.
Topic Breakdown
101 domains covering 121 questions
| Domain | Questions | Weight |
|---|---|---|
| Data Governance And Security | 7 | 6% |
| Streaming Data Processing | 5 | 4% |
| Optimizing Spark Applications | 3 | 2% |
| Managing Databricks Workflows | 2 | 2% |
| Ingesting And Transforming Data | 2 | 2% |
| Data Transformation And Processing | 2 | 2% |
| Delta Lake Data Management | 2 | 2% |
| Data Storage And Management | 2 | 2% |
| Data Ingestion | 2 | 2% |
| Security And Governance | 2 | 2% |
| Orchestrating Data Pipelines | 2 | 2% |
| Data Governance And Metadata Management | 1 | 1% |
| Data Ingestion And Processing | 1 | 1% |
| Data Ingestion And Transformation | 1 | 1% |
| Data Lakehouse Design And Architecture | 1 | 1% |
| Data Management And Quality | 1 | 1% |
| Data Management And Schema Evolution | 1 | 1% |
| Data Manipulation And Transformation With Apache Spark | 1 | 1% |
| Data Manipulation With Delta Lake | 1 | 1% |
| Data Modeling And Data Warehousing Concepts | 1 | 1% |
| Data Modeling And Schema Evolution | 1 | 1% |
| Data Monitoring And Alerting | 1 | 1% |
| Data Orchestration | 1 | 1% |
| Data Pipeline Development | 1 | 1% |
| Data Pipelines And Workflows | 1 | 1% |
| Data Processing Optimization | 1 | 1% |
| Data Quality And Testing | 1 | 1% |
| Data Security And Governance | 1 | 1% |
| Data Storage And Management On Databricks | 1 | 1% |
| Data Streaming | 1 | 1% |
| Data Transformation | 1 | 1% |
| Data Transformation And Delivery For Analytics | 1 | 1% |
| Data Transformation And Loading | 1 | 1% |
| Databricks Cluster Management And Monitoring | 1 | 1% |
| Databricks Data Architecture | 1 | 1% |
| Databricks Infrastructure Planning | 1 | 1% |
| Databricks Job Orchestration And Parameterization | 1 | 1% |
| Databricks Jobs And Orchestration | 1 | 1% |
| Databricks Notebook Language Interoperability | 1 | 1% |
| Databricks Platform Operations | 1 | 1% |
| Databricks Repos And Version Control | 1 | 1% |
| Databricks Security And Access Control | 1 | 1% |
| Databricks Workspace Management | 1 | 1% |
| Delta Lake Architecture And Performance Optimization | 1 | 1% |
| Delta Lake Data Governance And Management | 1 | 1% |
| Delta Lake Data Retention And Governance | 1 | 1% |
| Delta Lake Features And Performance Optimization | 1 | 1% |
| Delta Lake Performance Optimization | 1 | 1% |
| Delta Lake Table Management | 1 | 1% |
| Delta Lake Table Operations | 1 | 1% |
| Delta Lakehouse Data Management And Optimization | 1 | 1% |
| Deploying And Operating Data Pipelines | 1 | 1% |
| Designing And Implementing Data Models On Databricks | 1 | 1% |
| Designing And Implementing Data Pipelines | 1 | 1% |
| Developing Data Pipelines | 1 | 1% |
| Environment Management | 1 | 1% |
| Implement And Manage Security | 1 | 1% |
| Implement Data Governance And Security | 1 | 1% |
| Implement Data Quality In Delta Live Tables | 1 | 1% |
| Implement Data Quality With Delta Live Tables | 1 | 1% |
| Implementing And Managing Delta Lake Tables | 1 | 1% |
| Manage Security And Access Control | 1 | 1% |
| Managing Databricks Workflows And Jobs | 1 | 1% |
| Managing Delta Lake Tables | 1 | 1% |
| Managing Libraries And Dependencies On Databricks | 1 | 1% |
| Ml Model Integration In Spark Data Pipelines | 1 | 1% |
| Monitoring And Optimization | 1 | 1% |
| Monitoring And Optimizing Spark Application Performance | 1 | 1% |
| Optimize Data Workloads On Databricks | 1 | 1% |
| Optimizing Data Lake Performance | 1 | 1% |
| Optimizing Data Lakehouse Performance | 1 | 1% |
| Optimizing Databricks Data Ingestion And Processing | 1 | 1% |
| Optimizing Delta Lake Tables | 1 | 1% |
| Optimizing Spark Workloads | 1 | 1% |
| Orchestrating Production Workloads | 1 | 1% |
| Performance Optimization | 1 | 1% |
| Performance Optimization And Monitoring | 1 | 1% |
| Pipeline Orchestration And Management | 1 | 1% |
| Python Programming Fundamentals | 1 | 1% |
| Query Performance Optimization | 1 | 1% |
| Real-Time Data Processing With Spark Structured Streaming | 1 | 1% |
| Schema Management In Delta Lake | 1 | 1% |
| Software Development Practices | 1 | 1% |
| Spark Application Monitoring And Troubleshooting | 1 | 1% |
| Spark Application Optimization And Monitoring | 1 | 1% |
| Spark Cluster Management And Fault Tolerance | 1 | 1% |
| Spark Performance Optimization | 1 | 1% |
| Streaming Data Processing Optimization | 1 | 1% |
| Testing Data Solutions | 1 | 1% |
| Administering Databricks Workspaces | 1 | 1% |
| Workload Management And Optimization | 1 | 1% |
| Building And Maintaining Data Pipelines | 1 | 1% |
| Building And Managing Data Pipelines With Delta Live Tables | 1 | 1% |
| Building And Managing Production Data Pipelines | 1 | 1% |
| Building And Managing Streaming Data Pipelines | 1 | 1% |
| Building And Managing Streaming Pipelines | 1 | 1% |
| Cluster Monitoring And Optimization | 1 | 1% |
| Code Management And Version Control | 1 | 1% |
| Configure And Manage Databricks Clusters And Spark Runtimes | 1 | 1% |
| Data Access And Security | 1 | 1% |
| Data Definition And Management | 1 | 1% |
Study Plans
Choose a study plan that matches your schedule and experience level
30 Days
Intensive Sprint
Week 1-2
- Master fundamentals: Data Governance And Security
- Read Databricks official documentation
- Complete 5 questions daily
Week 3
- Deep dive: Streaming Data Processing
- Review weak areas from results
- Take 2 full-length exams
Week 4
- Review all flagged questions
- Timed exams to build stamina
- Final revision of key concepts
60 Days
Balanced Approach
Week 1-2
- Survey all exam domains
- Set up study environment
- Begin with foundational topics
Week 3-4
- Focus: Data Governance And Security
- Focus: Streaming Data Processing
- 3 questions daily
Week 5-6
- Focus: Optimizing Spark Applications
- Hands-on labs if applicable
- Review explanations for wrong answers
Week 7-8
- Complete all 122 questions
- Identify and eliminate weak areas
- Take 3 full-length timed tests
90 Days
Comprehensive Study
Month 1
- Learn all exam domains at a comfortable pace
- Build strong foundational knowledge
- 2 questions daily
Month 2
- Deep dive into each domain
- Hands-on practice and labs
- Take weekly timed exams
Month 3
- Work through all 122 questions
- Identify and eliminate weak areas
- Take 3 full-length timed exams
CERTIFIED-DATA-ENGINEER-PROFESSIONAL-Specific Tips
- Focus heavily on Delta Lake internals (ACID, time travel, schema evolution) — these appear across multiple questions. Run hands-on labs in Databricks Community Edition.
- Practice Structured Streaming patterns with stateful operations; understand windowing, watermarks, and exactly-once semantics — this is a high-weight topic.
- Deep-dive on Databricks Jobs orchestration and cluster configurations; the exam tests practical deployment scenarios, not just theory.
- Master Performance Optimization: partitioning strategies, caching, broadcast joins, and adaptive query execution. Test with real datasets >1GB.
- Study Access Control and Data Retention policies in detail — these are operational reality checks that distinguish experienced engineers from novices.
- Take full-length practice exams (aim for 100+ questions from the available 123). The 2.4 difficulty means you need 85%+ to pass confidently.
- Build a mini-project: ingest messy data, apply schema evolution, optimize queries, set retention policies, and implement Streaming — connects all domains.
Relevant Career Roles
Sample Questions
Try 5 free questions from the CERTIFIED-DATA-ENGINEER-PROFESSIONAL question bank
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table. The following logic is used to process these records. MERGE INTO customers USING ( SELECT updates.customer_id as merge_ey, updates .* FROM updates UNION ALL SELECT NULL as merge_key, updates .* FROM updates JOIN customers ON updates.customer_id = customers.customer_id WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?
The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes. A junior engineer has written the following code to add CHECK constraints to the Delta Lake table: A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed. Which statement explains the cause of this failure?
Which statement describes the correct use of pyspark.sql.functions.broadcast?
The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels. The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams. Which statement exemplifies best practices for implementing this system?
A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline. Which command should the data engineer enter in a web terminal configured with the Databricks CLI?
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