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CERTIFIED-DATA-ENGINEER-PROFESSIONAL Real Exam Questions

Databricks Certified Data Engineer Professional. Everything you need to prepare, practice, and pass.

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101

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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

DomainQuestionsWeight
Data Governance And Security76%
Streaming Data Processing54%
Optimizing Spark Applications32%
Managing Databricks Workflows22%
Ingesting And Transforming Data22%
Data Transformation And Processing22%
Delta Lake Data Management22%
Data Storage And Management22%
Data Ingestion22%
Security And Governance22%
Orchestrating Data Pipelines22%
Data Governance And Metadata Management11%
Data Ingestion And Processing11%
Data Ingestion And Transformation11%
Data Lakehouse Design And Architecture11%
Data Management And Quality11%
Data Management And Schema Evolution11%
Data Manipulation And Transformation With Apache Spark11%
Data Manipulation With Delta Lake11%
Data Modeling And Data Warehousing Concepts11%
Data Modeling And Schema Evolution11%
Data Monitoring And Alerting11%
Data Orchestration11%
Data Pipeline Development11%
Data Pipelines And Workflows11%
Data Processing Optimization11%
Data Quality And Testing11%
Data Security And Governance11%
Data Storage And Management On Databricks11%
Data Streaming11%
Data Transformation11%
Data Transformation And Delivery For Analytics11%
Data Transformation And Loading11%
Databricks Cluster Management And Monitoring11%
Databricks Data Architecture11%
Databricks Infrastructure Planning11%
Databricks Job Orchestration And Parameterization11%
Databricks Jobs And Orchestration11%
Databricks Notebook Language Interoperability11%
Databricks Platform Operations11%
Databricks Repos And Version Control11%
Databricks Security And Access Control11%
Databricks Workspace Management11%
Delta Lake Architecture And Performance Optimization11%
Delta Lake Data Governance And Management11%
Delta Lake Data Retention And Governance11%
Delta Lake Features And Performance Optimization11%
Delta Lake Performance Optimization11%
Delta Lake Table Management11%
Delta Lake Table Operations11%
Delta Lakehouse Data Management And Optimization11%
Deploying And Operating Data Pipelines11%
Designing And Implementing Data Models On Databricks11%
Designing And Implementing Data Pipelines11%
Developing Data Pipelines11%
Environment Management11%
Implement And Manage Security11%
Implement Data Governance And Security11%
Implement Data Quality In Delta Live Tables11%
Implement Data Quality With Delta Live Tables11%
Implementing And Managing Delta Lake Tables11%
Manage Security And Access Control11%
Managing Databricks Workflows And Jobs11%
Managing Delta Lake Tables11%
Managing Libraries And Dependencies On Databricks11%
Ml Model Integration In Spark Data Pipelines11%
Monitoring And Optimization11%
Monitoring And Optimizing Spark Application Performance11%
Optimize Data Workloads On Databricks11%
Optimizing Data Lake Performance11%
Optimizing Data Lakehouse Performance11%
Optimizing Databricks Data Ingestion And Processing11%
Optimizing Delta Lake Tables11%
Optimizing Spark Workloads11%
Orchestrating Production Workloads11%
Performance Optimization11%
Performance Optimization And Monitoring11%
Pipeline Orchestration And Management11%
Python Programming Fundamentals11%
Query Performance Optimization11%
Real-Time Data Processing With Spark Structured Streaming11%
Schema Management In Delta Lake11%
Software Development Practices11%
Spark Application Monitoring And Troubleshooting11%
Spark Application Optimization And Monitoring11%
Spark Cluster Management And Fault Tolerance11%
Spark Performance Optimization11%
Streaming Data Processing Optimization11%
Testing Data Solutions11%
Administering Databricks Workspaces11%
Workload Management And Optimization11%
Building And Maintaining Data Pipelines11%
Building And Managing Data Pipelines With Delta Live Tables11%
Building And Managing Production Data Pipelines11%
Building And Managing Streaming Data Pipelines11%
Building And Managing Streaming Pipelines11%
Cluster Monitoring And Optimization11%
Code Management And Version Control11%
Configure And Manage Databricks Clusters And Spark Runtimes11%
Data Access And Security11%
Data Definition And Management11%

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

Data Engineer (Databricks-specialized)Lakehouse ArchitectETL/ELT Developer (cloud-native)Data Platform EngineerAnalytics Engineer (Databricks stack)

Sample Questions

Try 5 free questions from the CERTIFIED-DATA-ENGINEER-PROFESSIONAL question bank

Q1Data Transformation and Processing

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?

Q2Data Management and Quality

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?

Q3Optimizing Spark Applications

Which statement describes the correct use of pyspark.sql.functions.broadcast?

Q4Data Governance and Security

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?

Q5Pipeline Orchestration and Management

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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