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Databricks Certified Data Engineer Associate Exam Overview
Struggling to prepare for the Databricks Data Engineer Associate exam? This complete guide breaks down everything you need to know so you can study smarter and pass with confidence.
What is the Databricks Certified Data Engineer Associate Certification?
The Databricks Certified Data Engineer Associate certification validates your ability to complete basic data engineering tasks using Databricks and the Lakehouse architecture. It proves you have a solid understanding of Spark SQL, Delta Lake, pipeline development, and production-grade workflows on the Databricks Lakehouse Platform.
This exam is ideal for individuals who are relatively new to Databricks but want to demonstrate foundational knowledge and hands-on skills with data processing and orchestration using core Databricks technologies.
Who Is This Certification For?
This certification is designed for:
- Data engineers looking to build or validate foundational skills in Databricks
- Analytics engineers and pipeline developers working on ELT processes
- SQL and Python developers transitioning into big data engineering roles
- Cloud professionals looking to expand into data lakehouse systems
- New graduates or professionals entering the data engineering field
Whether you’re building your credentials or aiming for promotion, this certification signals to employers that you can successfully handle production-ready workloads on Databricks.
What Job Roles Can This Certification Benefit?
Passing the Databricks Certified Data Engineer Associate exam can support roles such as:
- Data Engineer
- Analytics Engineer
- BI Developer
- ETL Developer
- Cloud Data Engineer
- Machine Learning Engineer (Data Prep)
- Platform Engineer with data expertise
This credential is valued in organizations migrating to the Databricks Lakehouse or seeking to modernize their data processing pipelines using Spark.
What Exam Version Should I Take?
There is just one live version of the Databricks Certified Data Engineer Associate exam—no separate versions for recertification. As of June 1, 2024, the exam blueprint reflects the current live content.
How Much Does the Exam Cost?
The exam costs $200 USD, which may vary slightly depending on your location and applicable taxes.
How Many Questions Are on the Exam?
The certification contains 45 multiple-choice questions, with some unscored items included for research. These unscored questions do not affect your final score, and extra time is already accounted for.
How Much Time Do I Have to Complete the Exam?
You’ll get 90 minutes to complete the exam. Manage your time carefully—some questions may require code interpretation or pipeline troubleshooting.
What Languages Is the Exam Available In?
The test is currently offered in:
- English
- Japanese (日本語)
- Portuguese (Português BR)
- Korean (한국어)
What’s the Passing Score?
The exam is scored on a raw scale, with 70 out of 100 as the passing benchmark.
Is the Exam Difficult?
Although it's an associate-level exam, the Databricks Certified Data Engineer Associate is rigorous. You’ll need to understand both SQL and Python-based data engineering workflows, handle error scenarios, and reason through best practices in the Databricks Lakehouse Platform.
Many questions are scenario-based, requiring you to apply knowledge across multiple layers of the Databricks ecosystem. Hands-on practice is critical.
To prepare effectively, we recommend using real-world simulated Databricks Certified Data Engineer Associate practice exams that reflect both the technical difficulty and style of the actual test.
What Domains Does the Exam Cover?
The exam features questions from five weighted domains:
-
Databricks Lakehouse Platform (24%)
- Control plane vs data plane
- Workspace setup & notebook environment
- Versioning, repos, clusters, and architecture
-
ELT with Spark SQL and Python (29%)
- Data ingestion, views, deduplication
- SQL functions, joins, parsing, and UDFs
- Structs, arrays, JSON, pivoting, control flow
-
Incremental Data Processing (22%)
- Delta Lake ACID compliance
- Z-Ordering, vacuum, Optimize, CTAS
- Version control, schema evolution, COPY INTO
-
Production Pipelines (16%)
- Job scheduling, CRON, retries, alerts
- Multi-task job setup and task dependencies
- Monitoring and debugging failed production pipelines
-
Data Governance (9%)
- Unity Catalog components and security modes
- Role-based access control
- Service principals, secure clusters, and catalogs
Are There Any Prerequisites?
There are no formal prerequisites, but Databricks recommends candidates have:
- At least 6 months of hands-on experience with the Databricks Lakehouse
- Familiarity with SQL and Python
- Exposure to Delta Lake, Apache Spark, ETL, and orchestration tools like DLT and Workflows
- Understanding of how data governance works in Unity Catalog
What Areas Should I Focus On?
Key knowledge areas to master include:
-
Databricks Notebooks & Repos
- Source control via Git and Repos
- Running notebooks inside Jobs
-
Apache Spark and Delta Lake
- Tables, data formats, ingest techniques
- Deduplication, joins, handling NULLs
- Merge, overwrite, CDC, rollback, OPTIMIZE, and VACUUM
-
Streaming and Batch Pipelines
- Auto Loader, structured streaming, continuous vs triggered mode
- Expectations and constraint behavior
-
DLT (Delta Live Tables)
- Pipeline configuration
- Using LIVE and STREAM constructs
-
Production Jobs via Databricks Workflows
- DAG logic with task dependencies
- CRON, alerts, retries, error diagnostics
-
Unity Catalog & Governance
- Securables: catalogs, schemas, tables
- Role grants, secure clusters, namespaces
Common Mistakes to Avoid
Avoid these common pitfalls:
- Skipping hands-on practice — real tasks with notebooks and tables are essential
- Not reviewing Unity Catalog security measures — these account for nearly 10% of the exam
- Ignoring batch vs stream nuances in pipeline architecture
- Relying solely on SQL if you're weak in Python — the exam uses both
- Forgetting the basics, like how to create views or perform joins
You’ll also want to take professional-grade Databricks Engineered practice exams to simulate the real exam and identify your weak areas.
How Do I Prepare for the Exam?
Databricks offers a mix of self-paced and instructor-led resources:
-
Official Training Courses
- “Data Engineering With Databricks” (Instructor-led or Self-paced)
- Delta Live Tables, Unity Catalog governance, and ingestion via Academy modules
-
Databricks Academy
- Practice datasets
- Notebook-based coding exercises
-
Practice Environment
- Use a free or corporate Databricks workspace
- Build ELT pipelines, stream with Auto Loader, and test rollback/versioning features
-
Documentation
- Deep-dive on ACID transactions, Delta Lake APIs
- Unity Catalog and Jobs API
How Long Is the Certification Valid?
Once achieved, your certification will remain valid for 2 years. You'll need to retake the full current exam to renew after that period.
Where Can I Take the Exam?
The Databricks Certified Data Engineer Associate exam is administered via online proctoring. You’ll need:
- Stable internet connection
- Webcam and microphone
- Quiet, distraction-free environment
- Ability to install proctoring software and meet system requirements
How Do I Register for the Exam?
The process is simple:
- Visit the official Databricks Certified Data Engineer Associate exam page
- Create an account or sign in
- Select your preferred language and test date
- Review technical requirements and complete payment
- Check your email for final instructions to begin your exam
What Happens If I Don’t Pass?
If you don’t pass the first time:
- Review your score breakdown to discover weak areas
- Strengthen those skills using hands-on labs and documentation
- Retake the exam after a short waiting period
Databricks does not publicly enforce a specific wait time, but it’s best to ensure sufficient preparation before your retry.
What Should I Take After This Certification?
Your next step may be the Databricks Certified Data Engineer Professional certification. It covers advanced topics like performance tuning, complex streaming pipelines, and system-level operations.
Alternatively, consider branching into roles like:
- Machine Learning Engineer (via Databricks ML tools)
- Databricks SQL Developer
- Data Platform Architect on Databricks
These roles build on the foundations certified in this exam.
Mastering this certification opens the door to modern data engineering roles and proves your ability to work efficiently in the Databricks Lakehouse environment. Prepare smartly, practice consistently, and soon you’ll be Databricks certified. Best of luck on your exam journey!
