Databricks Associate Skill Builder -- Exam Prep Course

Module 1: Exam Structure & Study Strategy

• Databricks Associate Data Engineer exam: domains, question count, and passing score
• Study plan design: 4-week schedule balancing concepts, practice, and revision
• Exam environment: proctoring requirements, allowed resources, and time management
• Identifying knowledge gaps: self-assessment quiz across all exam domains

Module 2: Databricks Lakehouse Platform Domain

• Lakehouse benefits: concurrency, reliability, and BI + ML on a single platform
• Cluster types and use cases: All-Purpose, Jobs, and SQL warehouse selection
• Databricks Runtime versions: difference between DBR, ML Runtime, and Photon
• Practice questions: 20 lakehouse platform domain questions with explanations

Module 3: ELT with Apache Spark Domain

• Spark transformations vs actions: understanding lazy evaluation for exam questions
• DataFrame operations: filter, join, groupBy -- common exam question patterns
• Spark SQL: writing correct multi-table queries under exam time pressure
• Practice questions: 25 Spark ELT questions covering common trick scenarios

Module 4: Incremental Data Processing Domain

• Auto Loader: file discovery, schema inference, and checkpoint management
• Structured Streaming: triggers, watermarks, and output modes for exam
• MERGE INTO: upsert pattern syntax and common exam edge cases
• Practice questions: 20 incremental processing questions with detailed solutions

Module 5: Production Pipelines Domain

• Delta Live Tables: expectations, pipeline modes, and table types for exam
• Databricks Workflows: task dependencies, retries, and notification settings
• Job clusters vs All-Purpose clusters: cost and isolation trade-offs
• Practice questions: 20 production pipeline questions with answer walkthroughs

Module 6: Data Governance Domain

• Unity Catalog hierarchy: metastore, catalog, schema, table -- exam-level detail
• GRANT syntax: table, schema, and catalog-level permissions for exam scenarios
• Dynamic views and row filters: column masking patterns tested in the exam
• Practice questions: 15 governance and security questions with full explanations

Module 7: Full Mock Exam -- Attempt 1

• Mock exam 1: 45 timed questions simulating real exam difficulty and format
• Answer review session: detailed explanation for every correct and incorrect answer
• Weak area identification: mapping wrong answers to specific knowledge domains
• Revision checklist: prioritised topics to revisit before the second mock

Module 8: Full Mock Exam -- Attempt 2 & Final Revision

• Mock exam 2: 45 fresh timed questions with increased scenario complexity
• Comparison analysis: tracking score improvement from mock 1 to mock 2
• Last-mile revision: 50 high-frequency flashcard concepts for exam day
• Exam day strategy: time allocation, flagging questions, and review techniq

Experience in the Industry Gain expertise from Azure-certified data engineers who have built cloud data platforms using Azure Databricks, ADLS Gen2, Azure Data Factory, and Azure Synapse for enterprises on the Microsoft Azure ecosystem.

Backgrounds at the Top Our Azure Databricks trainers have delivered cloud data engineering projects at financial institutions, retail companies, and healthcare organisations leveraging the full Microsoft Azure data platform stack.

Clear & Effective Teaching Azure Databricks workspace setup, ADLS Gen2 integration, Delta Lake, ADF orchestration, Structured Streaming, Azure ML integration, and Unity Catalog governance are taught with real Azure data platform examples.

Hands-On Learning Focus Students build Azure data pipelines, work with Delta Lake on ADLS Gen2, orchestrate notebooks with ADF, stream from Event Hubs, and configure Unity Catalog through structured hands-on lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest Azure Databricks releases, Microsoft Fabric integration considerations, Unity Catalog on Azure enhancements, and evolving Azure data platform best practices.