Databricks Certification Guides

This section covers current Databricks certification guide roots across analytics, data engineering, machine learning, and GenAI. The pages focus on the platform behaviors these exams repeatedly test: Spark execution, Delta Lake semantics, SQL correctness and grain, pipeline reliability, model lifecycle, vector search, and governance trade-offs.

Published exam roots currently include Data Analyst Associate, Data Engineer Associate, Data Engineer Professional, GenAI Engineer Associate, Machine Learning Associate, and Machine Learning Professional. Start with the exam root that matches your role, then use the cheat sheet for last-mile review, the FAQ for study guidance, and the resources page when you need official certification references and primary platform documentation.

In this section

• Databricks Data Analyst Associate (DA-ASSOC)
  Free exam-focused coverage of Databricks SQL, joins, windows, dashboards, alerts, and analytics troubleshooting for DA-ASSOC.
  • DA-ASSOC Study Plan (30 / 60 / 90 Days)
    A practical DA-ASSOC study plan you can follow: 30-day intensive, 60-day balanced, and 90-day part-time schedules with weekly focus and SQL-first practice tips.
  • DA-ASSOC Cheatsheet — Databricks SQL: Joins, Windows, Dashboards & Quick Rules
    Last-mile DA-ASSOC review: high-yield SQL patterns (joins, windows, CTEs), common pitfalls, and Databricks SQL dashboard/alert best practices.
  • DA-ASSOC FAQ — Databricks Data Analyst Associate Questions Answered
    Common DA-ASSOC questions answered: prerequisites, what to focus on (SQL + dashboards), how long to study, and how to practice effectively.
• Databricks Data Engineer Associate (DE-ASSOC)
  Free exam-focused coverage of Spark SQL, DataFrames, Delta Lake, and ETL fundamentals for DE-ASSOC.
  • DE-ASSOC Study Plan (30 / 60 / 90 Days)
    A practical DE-ASSOC study plan you can follow: 30-day intensive, 60-day balanced, and 90-day part-time schedules with weekly focus, suggested hours/week, and tips for using the IT Mastery practice app.
  • DE-ASSOC Cheatsheet — Spark + Delta Lake ETL (Tables, SQL, and Quick Rules)
    Last-mile DE-ASSOC review: Spark SQL/DataFrames essentials, Delta Lake features (schema, time travel, merge), ETL patterns, and exam-style pickers. Includes code snippets, tables, and diagrams.
  • DE-ASSOC FAQ — Databricks Data Engineer Associate Questions Answered
    Common DE-ASSOC questions answered: prerequisites, what to focus on (Spark + Delta Lake), how long to study, and how to practice effectively.
• Databricks Data Engineer Professional (DE-PRO)
  Free exam-focused coverage of streaming, DLT, pipeline reliability, performance tuning, and operational troubleshooting for DE-PRO.
  • DE-PRO Study Plan (30 / 60 / 90 Days)
    A practical DE-PRO study plan you can follow: 30-day intensive, 60-day balanced, and 90-day part-time schedules with weekly focus, suggested hours/week, and practice-first tips for production pipeline questions.
  • DE-PRO Cheatsheet — DLT, Streaming, Performance & Reliability on Databricks
    Last-mile DE-PRO review: incremental pipelines, Structured Streaming (watermarks/checkpoints), Delta Live Tables concepts, performance tuning pickers (shuffle/skew/file layout), and production troubleshooting heuristics.
  • DE-PRO FAQ — Databricks Data Engineer Professional Questions Answered
    Common DE-PRO questions answered: prerequisites, what to focus on (streaming, DLT, performance), how long to study, and how to practice effectively.
• Databricks GenAI Engineer Associate (GENAI-ASSOC)
  Free exam-focused coverage of RAG, vector search, evaluation loops, deployment trade-offs, and governance for GENAI-ASSOC.
  • GENAI-ASSOC Study Plan (30 / 60 / 90 Days)
    A practical GENAI-ASSOC study plan you can follow: 30-day intensive, 60-day balanced, and 90-day part-time schedules with weekly focus and RAG-first practice tips.
  • GENAI-ASSOC Cheatsheet — RAG, Vector Search, Evaluation & Deployment on Databricks
    Last-mile GENAI-ASSOC review: embeddings and chunking, vector search relevance, RAG prompt patterns, evaluation loops, and production trade-offs (cost, latency, governance).
  • GENAI-ASSOC FAQ — Databricks GenAI Engineer Associate Questions Answered
    Common GENAI-ASSOC questions answered: prerequisites, what to focus on (RAG, vector search, evaluation), and how to practice effectively.
• Databricks Machine Learning Associate (ML-ASSOC)
  Free exam-focused coverage of feature engineering, training, evaluation, MLflow, and model lifecycle basics for ML-ASSOC.
  • ML-ASSOC Study Plan (30 / 60 / 90 Days)
    A practical ML-ASSOC study plan you can follow: 30-day intensive, 60-day balanced, and 90-day part-time schedules with weekly focus, suggested hours/week, and MLflow-first practice tips.
  • ML-ASSOC Cheatsheet — MLflow, Features, Training & Evaluation on Databricks
    Last-mile ML-ASSOC review: feature engineering patterns, train/test discipline, MLflow tracking and registry concepts, and evaluation pickers. Includes code snippets, tables, and diagrams.
  • ML-ASSOC FAQ — Databricks Machine Learning Associate Questions Answered
    Common ML-ASSOC questions answered: prerequisites, what to focus on (MLflow, features, evaluation), how long to study, and how to practice effectively.
• Databricks Machine Learning Professional (ML-PRO)
  Free exam-focused coverage of production ML pipelines, registry workflows, deployment strategies, monitoring, and governance for ML-PRO.
  • ML-PRO Study Plan (30 / 60 / 90 Days)
    A practical ML-PRO study plan you can follow: 30-day intensive, 60-day balanced, and 90-day part-time schedules with weekly focus and MLOps-first practice tips.
  • ML-PRO Cheatsheet — Production ML on Databricks (Features, Registry, Deployment, Monitoring)
    Last-mile ML-PRO review: feature pipeline patterns, MLflow registry and promotion workflows, batch vs online deployment pickers, monitoring/drift decision rules, and governance essentials.
  • ML-PRO FAQ — Databricks Machine Learning Professional Questions Answered
    Common ML-PRO questions answered: prerequisites, what to focus on (MLOps, governance, deployment), how long to study, and how to practice effectively.