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 DLT pipeline defines expectations on a bronze streaming table: @dlt.expect_or_fail('valid_timestamp', 'timestamp IS NOT NULL'), @dlt.expect_or_drop('valid_amount', 'amount > 0'). During a pipeline update, a batch of 10,000 records arrives where 50 records have null timestamps and 200 records have amount <= 0. What is the outcome? (Select one!)
AThe pipeline processes 9,750 records, drops 200 records with invalid amounts, and logs 50 violations for null timestamps
BThe pipeline update fails immediately when the first null timestamp is encountered, and no records are processed
CAll 10,000 records are processed, but 250 records are flagged in the data quality metrics
DThe pipeline update fails because 50 records violate the expect_or_fail constraint, and the entire batch is rolled back
Explanation
The expect_or_fail expectation causes the entire pipeline update to fail and roll back when ANY record violates the constraint. In this case, 50 records have null timestamps, violating the valid_timestamp constraint with expect_or_fail behavior. The pipeline will fail before completing the update, and no records from this batch will be committed, including records that would have passed all checks. The expect_or_drop constraint on amount would drop invalid records if the pipeline reached that stage, but it never does because expect_or_fail terminates the update first. The pipeline does not process any records or log violations; it fails completely. All constraints are enforced, but expect_or_fail takes precedence by stopping the entire update.
2. A data engineering team creates a new catalog called analytics in Unity Catalog. The team grants CREATE TABLE privilege on schema analytics.sales to a group called data_engineers. A user in data_engineers attempts to create a table in analytics.sales but receives a permission denied error. What is the most likely cause? (Select one!)
AThe CREATE TABLE privilege must be granted at the catalog level, not schema level
BThe user needs ALL PRIVILEGES on the schema to create tables
CThe user needs MODIFY privilege on the schema in addition to CREATE TABLE
DThe user needs USE CATALOG privilege on the analytics catalog and USE SCHEMA privilege on the analytics.sales schema
Explanation
To create a table in Unity Catalog, users require three privileges: USE CATALOG on the parent catalog, USE SCHEMA on the parent schema, and CREATE TABLE on the schema. Even though the user has CREATE TABLE on analytics.sales, without USE CATALOG on analytics and USE SCHEMA on analytics.sales, they cannot access the schema to perform the create operation. MODIFY is for updating data, not creating tables. CREATE TABLE can be granted at schema level. ALL PRIVILEGES is not required when specific privileges suffice.
3. A data team implements row-level security on the sales table where sales representatives should only see their own regional data, while managers see all regions. They create a filter function and apply it to the table. Users report that managers can see all data but sales representatives see no rows. Which filter function logic is incorrect? (Select one!)
ACREATE FUNCTION sales_filter(region STRING) RETURN CURRENT_USER() = 'manager@company.com' OR region = 'US-WEST'
BCREATE FUNCTION sales_filter(region STRING) RETURN IF(IS_ACCOUNT_GROUP_MEMBER('managers'), TRUE, region = CURRENT_USER())
CCREATE FUNCTION sales_filter(region STRING) RETURN IF(IS_ACCOUNT_GROUP_MEMBER('managers'), TRUE, region = (SELECT region FROM users WHERE email = CURRENT_USER()))
DCREATE FUNCTION sales_filter(region STRING) RETURN CASE WHEN IS_ACCOUNT_GROUP_MEMBER('managers') THEN TRUE ELSE region = (SELECT rep_region FROM user_regions WHERE user_email = CURRENT_USER()) END
Explanation
The first option incorrectly compares region (a geographic value like US-WEST) with CURRENT_USER() which returns an email address, causing the comparison to always fail for sales representatives. The correct approach is to join the region value with a mapping table or user attribute that stores which region each user is assigned to. The second and third options correctly query a mapping table to find the user's assigned region. The fourth option uses hardcoded logic which works but is not scalable. Row filters must return a boolean expression that correctly evaluates the relationship between the column value and user identity.
4. 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.
5. A table uses liquid clustering with CLUSTER BY (region, product_category, user_segment). The data team notices that query patterns have changed and now primarily filter by date and product_category. They want to update the clustering keys without rewriting existing data. Which command achieves this? (Select one!)
ADROP TABLE sales; CREATE TABLE sales CLUSTER BY (date, product_category) AS SELECT * FROM sales_backup
BOPTIMIZE sales ZORDER BY (date, product_category)
CALTER TABLE sales CLUSTER BY (date, product_category); followed by OPTIMIZE sales
DALTER TABLE sales CLUSTER BY (date, product_category); followed by OPTIMIZE sales FULL
Explanation
Liquid clustering allows changing clustering keys via ALTER TABLE without rewriting data immediately. The ALTER TABLE command updates the table metadata to use new clustering keys. Running OPTIMIZE after changing keys applies incremental clustering to new and modified data using the updated keys, but does not force a full table rewrite. OPTIMIZE FULL would force reclustering all data immediately, which contradicts the requirement to avoid rewriting existing data. Dropping and recreating the table causes downtime and rewrites all data. ZORDER BY cannot be used on tables with liquid clustering enabled—the features are mutually exclusive. The incremental nature of liquid clustering means old data gradually gets reclustered as it is modified or as OPTIMIZE runs, without requiring immediate full rewrites.
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