Databricks · DCDEP
Databricks Certified Data Engineer Professional Practice Tests
Validates advanced proficiency in building and optimizing production-grade data engineering solutions on Databricks, covering data processing with Delta Lake and Structured Streaming, data modeling using Medallion Architecture, Databricks tooling including Workflows and REST APIs, and security, governance, and deployment.
Exam Details
Practice Questions
628
≈ 13 practice exams
Duration
120 minutes
Passing Score
70%
Difficulty
ProfessionalLast Updated
Feb 2026
Databricks Certified Data Engineer Professional Practice Exam Preparation
Use this DCDEP practice exam to prepare for Databricks Certified Data Engineer Professional with realistic questions, detailed explanations, and focused study modes. The practice bank includes 628 questions for Databricks DCDEP, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to patterns in your missed answers. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
Exam Domain Breakdown
Data Processing30%
Data Modeling and Medallion Architecture20%
Workflow Orchestration20%
Security and Governance15%
Performance Optimization and Monitoring10%
DevOps and CI/CD5%
Exam Overview
The Databricks Certified Data Engineer Professional certification validates advanced proficiency in building, optimizing, and maintaining production-grade data engineering solutions on the Databricks Data Intelligence Platform. Successful candidates demonstrate deep expertise across core platform capabilities including Delta Lake, Unity Catalog, Auto Loader, Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables), Databricks Compute (including serverless), Lakeflow Jobs, and the Medallion Architecture. The exam was updated in 2025 to reflect a Data Intelligence Platform framing, with expanded coverage of AI-driven features, Delta Sharing, Lakehouse Federation, Databricks Asset Bundles (DAB), and enhanced Unity Catalog governance.
This certification assesses the ability to design secure, reliable, and cost-effective ETL pipelines; process complex data from diverse sources using Python and SQL; implement Change Data Capture (CDC), SCD1, and SCD2 patterns; and apply best practices in schema management, observability, performance optimization, and data governance. Candidates are also evaluated on streaming workloads using Structured Streaming, workflow orchestration via Databricks Workflows, and deployment automation using the Databricks CLI, REST API, and Asset Bundles.
Who Should Take This Exam
This certification is designed for experienced data engineering professionals with at least one year of hands-on experience building and operating production data pipelines on Databricks. Ideal candidates include Senior Data Engineers, Lead Analytics Engineers, Data Architects, and Big Data professionals who work daily with Apache Spark, Delta Lake, and the Databricks Lakehouse Platform. Those who have already obtained the Databricks Certified Data Engineer Associate credential are especially well-positioned, as the Professional exam builds substantially on that foundational knowledge.
Professionals transitioning from traditional ETL development, data scientists who regularly build and maintain pipelines, and Solutions Architects seeking to validate deep platform expertise are also strong candidates. Code examples on the exam are primarily in Python and SQL, so comfort with PySpark and Spark SQL is essential.
Prerequisites
There are no formal prerequisite certifications required, but Databricks strongly recommends holding or demonstrating mastery of the skills covered by the Databricks Certified Data Engineer Associate certification before attempting the Professional exam. Candidates should have at least one year of hands-on experience performing the data engineering tasks outlined in the official exam guide.
Recommended knowledge areas include proficiency with Apache Spark (PySpark and Spark SQL), Delta Lake operations (MERGE, OPTIMIZE, ZORDER, VACUUM, Change Data Feed), Structured Streaming concepts (Auto Loader, windowing, watermarking), Unity Catalog for data governance, Databricks Workflows for job orchestration, and familiarity with DevOps practices including version control and CI/CD pipelines. Candidates should be comfortable working in the Databricks Workspace, using the Databricks CLI and REST API, and applying the Medallion Architecture (Bronze, Silver, Gold layers) in real-world pipeline design.
Exam Format
The Databricks Certified Data Engineer Professional exam consists of approximately 60 scored multiple-choice questions, with some candidates reporting up to 65 questions. The exam duration is 120 minutes (2 hours). As with other Databricks exams, the form may include a small number of unscored survey items used to gather statistical data for future exam development; these are not identified and do not affect the final score, and additional time is factored in to account for them.
The exam is delivered online via a remote proctoring platform and costs $200 USD (plus applicable taxes). The passing score is 70%. Questions are scenario-based and require applied knowledge rather than rote memorization, frequently presenting realistic production engineering challenges in PySpark and SQL. Recertification is required every two years by retaking the current version of the exam.
Skills Measured
- 1.Data Processing (~30%): Building robust batch and incremental ETL pipelines; deduplicating data; implementing Change Data Capture (CDC) using Delta Lake Change Data Feed (CDF) and Lakeflow Spark Declarative Pipelines; applying Structured Streaming with Auto Loader, windowing, and watermarking; performing Delta Lake operations including MERGE, OPTIMIZE, ZORDER, and VACUUM; designing SCD Type 1 and Type 2 patterns.
- 2.Data Modeling and Medallion Architecture (~20%): Designing and implementing the Medallion Architecture (Bronze, Silver, Gold layers); applying lakehouse data modeling principles; managing schemas and schema evolution; using Unity Catalog for multi-workspace data organization and access control.
- 3.Workflow Orchestration (~20%): Configuring Databricks Workflows (Jobs and Tasks) for complex pipeline dependencies; implementing orchestration patterns including fan-out, funnel, and sequential execution; using dbutils for file and secret management; integrating Databricks Repos and version control; deploying and managing jobs via the Databricks CLI and REST API.
- 4.Security and Governance (~15%): Implementing fine-grained access control with Unity Catalog including row-level and column-level security; configuring data lineage tracking and audit logging; applying Delta Sharing for secure cross-platform data exchange; managing secrets and credentials; understanding Lakehouse Federation for querying external data sources.
- 5.Performance Optimization and Monitoring (~10%): Tuning Spark workloads for throughput and cost efficiency; applying Delta Lake performance features (file compaction, Z-ordering, liquid clustering); monitoring streaming query health using StreamingQueryListener; implementing observability best practices for production pipelines.
- 6.DevOps and CI/CD (~5%): Implementing CI/CD workflows for Databricks using Databricks Asset Bundles (DAB); automating deployment with the Databricks CLI and REST API; writing and running tests using frameworks like pytest; managing infrastructure-as-code for Databricks resources.
Study Tips
- Download and study the official Databricks Certified Data Engineer Professional Exam Guide PDF from the Databricks certification page — it defines exact domains, topic areas, and sample questions. Verify you have the most current version (updated March 2025) a few weeks before your exam date.
- Complete the 'Advanced Data Engineering with Databricks' course on Databricks Academy, which is specifically designed for this professional-level certification. Supplement with self-paced courses on Streaming and Lakeflow Spark Declarative Pipelines, Databricks Data Privacy, Performance Optimization, and Automated Deployment with Databricks Asset Bundles.
- Practice hands-on in a Databricks workspace (Community Edition or trial) — focus specifically on Delta Lake MERGE operations, CDC pipelines using Change Data Feed, Auto Loader ingestion patterns, and configuring multi-task Workflows. Scenario-based questions heavily test applied knowledge over theory.
- Study Unity Catalog in depth, including workspace-level vs. metastore-level permissions, row/column-level security, data lineage, audit logging, and Delta Sharing. The 2025 exam update significantly expanded Unity Catalog coverage.
- Take the official Databricks practice exams available through the certification portal to calibrate your readiness. Aim to score consistently above 80% on practice tests before sitting the exam, given that the official passing threshold is 70% but production-grade scenario questions demand deep understanding.
- Focus on streaming architecture patterns — understand exactly when to use Structured Streaming vs. Lakeflow Spark Declarative Pipelines, how to handle late data with watermarks, how to configure stateful aggregations, and how to monitor streaming jobs using StreamingQueryListener and the Workflows UI.
- Review Databricks Asset Bundles (DAB) and the Databricks CLI for deployment automation, as the 2025 exam update added increased emphasis on DevOps, CI/CD, and programmatic deployment of Databricks resources.
Career Benefits
The Databricks Certified Data Engineer Professional credential is recognized as an advanced-tier validation of Lakehouse Platform expertise, positioning holders for senior roles such as Senior Data Engineer, Lead Analytics Engineer, Data Architect, and Solutions Architect. Databricks is used by more than 7,000 organizations globally, including approximately 40% of Fortune 500 companies, creating sustained demand for certified professionals. Certified data engineers in the US typically earn between $115,000 and $150,000 annually, with top earners exceeding $160,000 depending on experience, location, and industry. Glassdoor data places the average at approximately $131,000, with a range extending to $170,000 at the 75th percentile.
Compared to the Associate-level certification, the Professional credential signals the ability to architect and operate enterprise-grade solutions — not just implement them — which substantially increases leverage in salary negotiations and job applications. The certification also serves as a differentiator against candidates holding generalist cloud data engineering credentials (e.g., AWS, Azure, GCP data engineer certs), as Databricks expertise is platform-specific and increasingly in demand as organizations adopt the Lakehouse architecture for unified analytics and AI workloads. Recertification every two years ensures holders stay current with the rapidly evolving platform.
Sample Questions
5 sample questions with answers and explanations. The full bank has 628 questions, enough for 13 full-length practice exams.
Preview — answers shown1. A notebook running on a job cluster needs to write Parquet files to /Volumes/prod/analytics/reports/ and read configuration from a secrets scope named prod_config. The job cluster is configured with a service principal. What minimum permissions must the service principal have? (Select two!)
Multiple correct answersAWRITE VOLUME on the reports volume
BMODIFY on the analytics schema
CUSE CATALOG and USE SCHEMA on prod.analytics
DREAD VOLUME on the reports volume
ESecrets READ permission on the prod_config scope
Explanation
Writing to a volume requires WRITE VOLUME privilege on the specific volume. Reading secrets requires the service principal to have READ permission on the secrets scope, configured separately through the Databricks secrets API or CLI. The WRITE VOLUME privilege allows creating, modifying, and deleting files within the volume path. While USE CATALOG and USE SCHEMA are required to discover and navigate to the volume, the question asks specifically for permissions to write files and read secrets. MODIFY permission applies to tables (insert, update, delete data), not volumes. READ VOLUME would be needed if the job also needed to read files from the volume, but the scenario only mentions writing. The minimum permissions are WRITE VOLUME for file operations plus secrets READ for configuration access.
2. A Databricks workspace uses system tables to monitor audit events. A security team wants to identify all instances where users granted SELECT permissions on tables in the sensitive_data catalog over the past 30 days. Which query correctly retrieves this information from system tables? (Select one!)
ASELECT event_time, user_identity.email, request_params.securable_full_name FROM system.access.audit WHERE action_name = 'grantPermissions' AND request_params.securable_type = 'TABLE'
BSELECT event_time, user_identity.email FROM system.access.audit WHERE request_params.privileges CONTAINS 'SELECT' AND catalog = 'sensitive_data'
CSELECT event_time, user_identity.email, request_params.changes FROM system.access.table_lineage WHERE catalog_name = 'sensitive_data' AND operation_type = 'GRANT'
DSELECT event_time, user_identity.email, request_params.securable_full_name FROM system.access.audit WHERE service_name = 'unityCatalog' AND action_name = 'updatePermissions' AND request_params.securable_full_name LIKE 'sensitive_data.%'
Explanation
The system.access.audit table captures permission changes with service_name of unityCatalog and action_name of updatePermissions. Filtering by securable_full_name using LIKE 'sensitive_data.%' ensures only objects within the sensitive_data catalog are included. The action_name grantPermissions does not exist; permission grants are recorded as updatePermissions. The system.access.table_lineage table tracks data lineage and read/write operations, not permission changes. The audit table does not have a simple catalog column; the catalog is part of securable_full_name, and CONTAINS is not valid syntax for array filtering in this context.
3. A streaming job processes IoT data and must recover from failures automatically while minimizing costs. The job should allow only one instance to run at a time to prevent duplicate processing. Which job configuration meets these requirements? (Select two!)
Multiple correct answersAUse an all-purpose cluster with autoscaling enabled
BConfigure a new job cluster for the streaming task
CSet Max Concurrent Runs to 1
DSet Retries to Unlimited
EEnable spot instances with no retry policy
Explanation
Setting Max Concurrent Runs to 1 ensures only one instance of the job runs at a time, preventing duplicate processing of streaming data. Setting Retries to Unlimited allows the job to automatically recover from transient failures, which is critical for long-running streaming workloads. Together, these configurations provide automatic recovery while maintaining exactly-once processing semantics. Using a new job cluster rather than an all-purpose cluster is recommended for cost savings (40-60% lower DBU rates), but the question asks specifically about recovery and concurrency controls. All-purpose clusters are more expensive and designed for interactive development, not production streaming. Spot instances can reduce costs but without proper retry configuration can lead to data loss. The combination of single concurrency and unlimited retries is the key to meeting the stated requirements.
4. A data engineer troubleshoots a slow aggregation query on a large fact table. Using the Spark UI, they observe that most tasks complete in 30 seconds, but 5 tasks take over 10 minutes. The query includes a GROUP BY on customer_id column. What performance issue does this pattern indicate? (Select one!)
AInsufficient cluster memory causing disk spilling
BNetwork congestion between executor nodes
CMissing Z-Order optimization on the customer_id column
DData skew where certain customer_id values have disproportionately more records
Explanation
The pattern of most tasks completing quickly while a few tasks take significantly longer is a classic indicator of data skew. When grouping by customer_id, if certain customers have far more transactions than others, the partitions containing those customer IDs will be much larger, causing those specific tasks to take much longer. Insufficient memory would cause more uniform slowness across tasks as all executors would experience spilling. Network congestion would affect task completion more uniformly. Missing Z-Order optimization affects file pruning during reads, not task execution time distribution during aggregation. The solution would be to enable AQE skew join optimization or use salting techniques to redistribute skewed keys.
5. A data platform team evaluates whether to use liquid clustering or traditional partitioning with Z-ordering for a 15 TB sales table. Queries filter on product_id (10M distinct values), region (50 distinct values), and sale_date (3 years daily). Query patterns change frequently as business requirements evolve. Which strategy should they choose? (Select one!)
APartition by region, Z-ORDER by (sale_date, product_id) for low cardinality partition column
BPartition by sale_date, Z-ORDER by (product_id, region) for optimal read performance
CUse liquid clustering on sale_date only to match partitioning semantics
DUse liquid clustering on (product_id, region, sale_date) for flexible optimization
Explanation
Liquid clustering is recommended for this scenario because it handles high cardinality columns (product_id with 10M values), supports dynamic query patterns through ALTER TABLE CLUSTER BY without data rewrites, and scales well for large tables (15 TB). The table size and evolving query patterns make liquid clustering ideal. Partitioning by sale_date creates manageable partitions but requires table recreation to change strategies. Partitioning by high cardinality product_id creates excessive directories. Region-based partitioning locks the strategy and Z-ordering is not incremental. Single-column clustering underutilizes the optimization capability.
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