Databricks · DCDEP
Databricks Certified Data Engineer Professional Practice Test
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
Questions
628
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. Start a practice session to test yourself across all 628 questions.
Preview — answers shown1. A Delta table has 10,000 small files averaging 5 MB each after many incremental append operations. The table is configured with delta.autoOptimize.optimizeWrite = true and delta.autoOptimize.autoCompact = false. A new batch write adds 200 MB of data. What happens? (Select one!)
AThe write operation fails because autoCompact must be enabled for optimizeWrite to function
BThe write operation automatically compacts all 10,000 existing small files plus new data into 1 GB files
CThe write operation triggers background compaction of existing small files after new data is written
DThe write operation produces optimized file sizes for new data only; existing small files remain unchanged
Explanation
The optimizeWrite setting (delta.autoOptimize.optimizeWrite) optimizes file sizes during the write operation itself, before persisting data to storage. It affects only the data being written in the current operation, not existing files. This results in the new 200 MB being written in optimally-sized files (close to the 1 GB target), but does not compact the 10,000 existing 5 MB files. The autoCompact setting (delta.autoOptimize.autoCompact) runs compaction after write operations complete and would address existing small files. These settings are independent—either can be enabled without the other. optimizeWrite does not trigger background compaction; it only affects how new data is written. To compact existing small files, the table needs either autoCompact enabled or manual OPTIMIZE commands run.
2. A Databricks notebook must accept parameters from a Databricks Job and use different values in development versus production. The notebook creates a widget for the environment parameter with default value dev. When executed as a job task with parameter environment set to prod, the notebook still uses dev. What is the issue? (Select one!)
AThe notebook creates the widget after attempting to read the parameter value
BWidgets created with default values always use the default and ignore job parameters
CJob parameters override widget defaults only when the widget is created with dbutils.widgets.dropdown instead of dbutils.widgets.text
DJob parameters must be retrieved using dbutils.jobs.taskValues.get instead of dbutils.widgets.get
Explanation
Widgets must be created before retrieving their values. When a notebook runs as a job task, job parameters override widget defaults only if the widget exists at the time of parameter injection. If the code reads the widget value before creating it, or creates it with a default after reading, the job parameter is not applied. Job parameters are accessed via dbutils.widgets.get after widget creation, not through a separate API. The widget type (text, dropdown, etc.) does not affect parameter override behavior. Job parameters always override widget defaults when widgets are properly created before value retrieval.
3. A data governance team implements a row filter function on a sales table to restrict users to seeing only their own region's data. The filter function uses IS_ACCOUNT_GROUP_MEMBER to check group membership. A manager who belongs to both the 'analysts' and 'managers' groups reports seeing different row counts when querying the same table from different sessions. What is the most likely cause? (Select one!)
ARow filter functions are evaluated at query execution time based on current session context
BQuery result caching is returning stale results from previous sessions
CThe row filter function has conflicting conditions for multiple group memberships
DRow filters do not support users belonging to multiple groups
Explanation
Row filter functions are evaluated dynamically at query execution time based on the current user's session context and group memberships. If the user's effective group membership changes between sessions (such as by switching personas or credential contexts), the filter results will differ. This dynamic evaluation enables context-aware security. Query result caching uses cache keys that include user identity, preventing cross-user cache sharing. Row filter functions can handle multiple group memberships through boolean logic. Unity Catalog fully supports users belonging to multiple groups in row filter expressions.
4. A production DLT pipeline contains five streaming tables with data quality expectations. The bronze table has one expect rule for valid_timestamp. The silver table has three rules: two expect_or_drop rules and one expect_or_fail rule. When investigating data quality issues, where can the data engineer find metrics showing how many records failed each expectation rule? (Select one!)
ARun DESCRIBE DETAIL on each table to see expectation metrics in table properties
BQuery the information_schema.table_constraints table in Unity Catalog
CQuery the system.quality.expectations table for the pipeline
DNavigate to the DLT pipeline page in the UI, click each table, and view the data quality tab
Explanation
DLT pipeline data quality metrics are displayed in the Databricks UI by navigating to the pipeline page, selecting each table, and viewing the data quality statistics tab. This shows detailed metrics for each expectation including records passed, dropped, and failed. The information_schema.table_constraints table contains schema constraints but not DLT expectation metrics. DESCRIBE DETAIL shows table metadata but does not include row-level expectation statistics. While Databricks system tables provide pipeline event logs, the most direct way to view expectation metrics is through the pipeline UI, which aggregates and displays quality metrics per table. For programmatic access, query the event log using system tables, but the UI provides the most user-friendly view.
5. A production DLT pipeline has a streaming table called bronze_events with the following expectation: @dlt.expect_or_fail('valid_timestamp', 'event_time IS NOT NULL'). During a pipeline update, 1000 records arrive where 5 records have null event_time values. What happens to the pipeline and the data? (Select one!)
AThe pipeline update fails and all changes from this update are rolled back, including the 995 valid records
BAll 1000 records are written to bronze_events and the 5 invalid records are flagged in metrics
CThe pipeline fails immediately when the first invalid record is encountered and no records are written
DThe 995 valid records are written and the 5 invalid records are dropped from bronze_events
Explanation
The expect_or_fail expectation causes the entire pipeline update to fail and roll back all changes when any record violates the constraint. This is the strictest data quality enforcement level in DLT. Unlike expect which only tracks violations in metrics, or expect_or_drop which removes invalid records while allowing the pipeline to continue, expect_or_fail treats any constraint violation as a critical failure requiring immediate attention. When the pipeline encounters any of the 5 records with null event_time values, the entire update transaction fails and rolls back, meaning none of the 1000 records are written to the table. This ensures that the pipeline never processes data that violates critical quality requirements, but requires that upstream data sources are highly reliable or that data cleansing occurs before applying expect_or_fail constraints.
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