Databricks Certified Data Engineer Associate - DCDEA
The Databricks Certified Data Engineer Associate (DCDEA) certification validates your ability to build, manage, and optimize data pipelines using the Databricks Lakehouse Platform. This certification is ideal for professionals working with big data who want to demonstrate hands-on skills in data ingestion, transformation, and orchestration using Apache Spark and Databricks tools. Earning the DCDEA credential proves that you can work with batch and streaming data, manage data reliability, and support analytics and machine learning workloads at scale.
At Certify360.ai, we simplify your preparation for the Databricks Certified Data Engineer Associate exam with structured, AI-powered learning paths. Our platform converts complex Spark and Databricks concepts into clear, practical lessons, reinforced with real-world scenarios and adaptive practice tests. With personalized recommendations, hands-on exercises, and detailed performance analytics, Certify360 ensures you are exam-ready while building job-relevant data engineering skills.
Exam Overview
- Number of Questions: 60 questions (multiple choice or multiple response)
- Exam Duration: 120 minutes
- Exam Fee: 200 USD (may vary slightly based on region and currency exchange rates)
- Delivery Options: Online proctored exam, In-person at a Pearson VUE testing center
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Certification study guides for Databricks Certified Data Engineer Associate – DCDEA
- Architecting Solutions: Design scalable data engineering solutions using the Databricks Lakehouse architecture to support analytics, reporting, and machine learning workloads.
- Using Core Databricks Services: Get hands-on with Databricks notebooks, Apache Spark, Delta Lake, workflows, and Databricks SQL to build end-to-end data pipelines.
- Data Security: Learn how to secure data using access controls, table permissions, secrets management, and secure data sharing within the Databricks environment.
- Designing High Availability: Build reliable data pipelines using Delta Lake features such as ACID transactions, schema enforcement, and fault-tolerant Spark jobs.
- Cost Optimization: Optimize compute and storage costs by selecting appropriate cluster configurations, auto-scaling, and efficient query and pipeline design.
- Monitoring and Troubleshooting: Use Databricks job monitoring, Spark UI, and logging tools to diagnose failures, optimize performance, and ensure pipeline reliability.
Best resources for Databricks Certified Data Engineer Associate – DCDEA
Databricks Official Exam Guide – Covers exam objectives and skill expectations.
Databricks Academy Courses – Official self-paced learning paths for data engineering.
Apache Spark Documentation – Core reference for Spark transformations and actions.
Delta Lake Documentation – Best practices for reliable data lakes.
Certify360 Mock Tests & Exercises – Scenario-based assessments aligned with DCDEA exam patterns.
How to pass Databricks Certified Data Engineer Associate – DCDEA
Understand the Learning Outcomes
Focus on the core competency areas: Data Ingestion, Data Transformation, Data Orchestration, Data Storage with Delta Lake, and Data Reliability.
Study how Databricks enables scalable batch and streaming pipelines using Apache Spark and the Lakehouse architecture.Hands-On Practice
Build real or simulated pipelines using Databricks notebooks and Spark DataFrames.
Practice ingesting data from multiple sources, applying transformations, and writing optimized Delta tables.Take Practice Exercises
Use Certify360’s exercises, scenario-based case studies, and mock quizzes to strengthen both conceptual and practical understanding.
Analyze pipeline failures and performance bottlenecks through guided troubleshooting scenarios.Review Data Engineering Principles & Patterns
Revisit Spark concepts such as lazy evaluation, partitioning, and joins.
Explore best practices for Delta Lake, incremental processing, and pipeline orchestration using workflows.
Tips to pass Databricks Certified Data Engineer Associate – DCDEA
a. Understand the Exam Domains
Focus on these areas:
Data Ingestion & Transformation
Delta Lake & Data Reliability
Apache Spark & Databricks Tools
Orchestration & Monitoring
b. Use Official Databricks Resources
Databricks Academy learning paths
Databricks documentation and notebooks
Official exam guide and sample questions
c. Practice with Real-World Scenarios
Work on use cases involving ETL pipelines, incremental data loads, and data quality enforcement using Delta Lake.
d. Take Mock Tests on Certify360
Attempt adaptive quizzes, hands-on scenarios, and exam-style questions to build confidence and improve time management.
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