Exam Prep – Databricks Certified Data Engineer Associate
(1 day)
Course Description
This course prepares learners for the Databricks Certified Data Engineer Associate exam through an exam-style, question-driven approach. Each module introduces realistic practice questions covering the official exam domains. Instructors then walk through every option—why it is correct or incorrect—blending in targeted teaching to reinforce Databricks concepts. Learners finish the course confident in both exam readiness and their practical knowledge of Databricks tools.
Learning Objectives
By the end of this course, learners will be able to:
- Apply Databricks data engineering concepts through exam-style practice questions.
- Analyze answer options critically, understanding both correct reasoning and common mistakes.
- Strengthen knowledge of Databricks Lakehouse, Lakeflow, Unity Catalog, Delta Sharing, and governance tools.
- Build confidence in exam-day strategy through repeated exposure to realistic scenarios.
Audience
- Candidates preparing for the Databricks Certified Data Engineer Associate exam
- Data engineers with 6+ months of hands-on Databricks experience
- Professionals who prefer a practice-based, scenario-driven learning style
Prerequisites
- Familiarity with Python or SQL
- Hands-on experience with Databricks notebooks, clusters, and pipelines
- General understanding of data engineering workflows
Course Outline
Module 1: Exam Orientation and Strategy
- Certification format, domains, scoring, timing
- Strategies for analyzing multiple-choice questions
- Question types and common distractors
- Pacing strategies and flagging questions for review
Module 2: Databricks Intelligence Platform
- Value propositions of the Databricks Lakehouse
- Compute and cluster types, selection criteria
- Workspace features, interface navigation, and productivity tips
- Performance optimizations at the platform level
Module 3: Development and Ingestion
- Using Databricks Connect for local development
- Notebook functionality, utilities, and debugging approaches
- Auto Loader use cases, syntax, and schema evolution
- Best practices for ingestion workflows and error handling
Module 4: Data Processing and Transformations with Lakeflow
- Medallion architecture (Bronze, Silver, Gold layers)
- Designing and running Lakeflow pipelines for batch and streaming
- Writing transformations with PySpark DataFrames and Spark SQL
- Cluster configuration for performance optimization
- Handling schema changes and transformations across layers
Module 5: Productionizing Data Pipelines
- Databricks Asset Bundles (DAB) structure and usage
- Scheduling and orchestrating jobs workflows
- Error recovery, retries, and reruns
- Leveraging serverless compute for optimized performance
- Using Spark UI for performance analysis and troubleshooting
Module 6: Data Governance and Quality
- Differences between managed and external tables
- Unity Catalog hierarchy: Catalogs, schemas, tables, roles, and permissions
- Lineage and audit logging for compliance and traceability
- Delta Sharing: Features, advantages, cost considerations
- Lakehouse Federation use cases for external data access
