dbx-exam-guide

Databricks Data Engineer Associate Certification Study Plan

Phase 1: Foundations (Week 1-2)

Azure Databricks Workspace Basics

• Set up Azure Databricks workspace and explore UI
• Create and manage notebooks (Python, SQL, Scala)
• Practice using Databricks File System (DBFS)
• Understand cluster configuration options
• Create and terminate clusters

Apache Spark Fundamentals

• Learn Spark architecture (driver, executors, tasks)
• Study DataFrames vs RDDs
• Practice basic DataFrame operations (select, filter, groupBy)
• Understand lazy evaluation and transformations
• Study actions vs transformations

Phase 2: Data Processing (Week 3-4)

Reading and Writing Data

• Read CSV, JSON, Parquet files into DataFrames
• Write DataFrames to different formats
• Practice schema inference and explicit schemas
• Work with partitioned data
• Understand file format optimization (Parquet vs others)

Data Transformations

• Practice joins (inner, outer, left, right)
• Use aggregation functions (count, sum, avg, max, min)
• Apply window functions
• Handle missing/null values
• Work with complex data types (arrays, structs, maps)

PySpark and SQL

• Write equivalent PySpark and SQL queries
• Use SQL magic commands in notebooks
• Practice subqueries and CTEs
• Create and use temporary views
• Understand Spark SQL optimization

Phase 3: Delta Lake (Week 5-6)

Delta Lake Basics

• Understand ACID properties in Delta Lake
• Create Delta tables
• Convert Parquet tables to Delta
• Practice table versioning
• Use DESCRIBE HISTORY command

Delta Lake Operations

• Perform MERGE operations (upserts)
• Use time travel for querying historical data
• Practice OPTIMIZE and Z-ORDER operations
• Implement VACUUM to clean up old files
• Understand transaction log

Change Data Capture (CDC)

• Implement CDC patterns with Delta Lake
• Use Delta Lake CDF (Change Data Feed)
• Practice incremental data processing
• Handle updates and deletes

Phase 4: ETL Pipelines (Week 7-8)

Databricks Jobs

• Create single-task jobs
• Build multi-task workflows
• Configure job clusters vs all-purpose clusters
• Set up job scheduling
• Implement error handling and notifications

Incremental Processing

• Use Auto Loader for incremental ingestion
• Implement watermarking strategies
• Practice checkpointing
• Handle late-arriving data
• Work with trigger options (once, continuous, available-now)

Data Quality

• Implement data validation checks
• Use expectations and constraints
• Practice quarantine patterns for bad data
• Monitor data quality metrics
• Handle schema evolution

Phase 5: Advanced Topics (Week 9-10)

Performance Optimization

• Study partitioning strategies
• Practice broadcast joins
• Understand caching and persistence
• Optimize shuffle operations
• Read Spark UI to identify bottlenecks

Unity Catalog

• Understand three-level namespace (catalog.schema.table)
• Practice creating catalogs and schemas
• Configure managed vs external tables
• Implement access control with GRANT/REVOKE
• Use secure views

Databricks SQL and BI

• Create SQL warehouses
• Build queries in SQL editor
• Practice creating visualizations
• Understand serverless vs classic SQL warehouses
• Set up alerts and scheduled queries

Phase 6: Practice and Review (Week 11-12)

Practice Exams

• Take first practice exam (identify weak areas)
• Review incorrect answers
• Take second practice exam
• Time yourself on sample questions
• Review all flagged topics

Final Review

• Create cheat sheet of key concepts
• Review Delta Lake operations syntax
• Memorize cluster configuration best practices
• Review Auto Loader syntax and options
• Practice whiteboard architecture diagrams

Exam Day Preparation

• Review exam format and time limits
• Prepare technical setup for online exam
• Review ID requirements
• Do final 30-minute review of notes
• Schedule and take the exam!

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Daily Practice Tips:

• Spend 1-2 hours per day on study
• Code along with every concept
• Create a personal notebook collection
• Join Databricks community forums
• Review official documentation regularly

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