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ML-ASSOC Overview — What’s Tested, Common Traps & How to Prepare

Everything to know before the Databricks Machine Learning Associate (ML-ASSOC) exam: platform focus areas (MLflow, feature engineering, training), common traps, and a practical prep funnel.

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Exam snapshot (high level)

  • Certification: Databricks Certified Machine Learning Associate (ML‑ASSOC)
  • Audience: ML practitioners building models using Databricks and MLflow
  • Skills level: you should be comfortable with basic ML workflow steps and how Databricks/MLflow supports them
  • Official details: registration, pricing, and delivery mode can change—use Resources for current info.

Study funnel: Follow the Study Plan → work the Syllabus objective-by-objective → use the Cheatsheet for recall → validate with Practice.

What ML‑ASSOC measures (what you should be able to do)

1) Prepare data for ML on Databricks

  • Feature engineering with Spark/DataFrames and SQL.
  • Train/validation/test splits and leakage awareness (concept-level).

2) Train and evaluate models

  • Model selection basics (classification vs regression; metrics).
  • Cross-validation and hyperparameter tuning awareness (platform framing).

3) Track experiments with MLflow

  • Runs, parameters, metrics, artifacts, and reproducibility.
  • Comparing runs and choosing the best candidate.

4) Manage model lifecycle

  • Registering models, staging/promoting versions, and packaging artifacts.
  • Basic deployment concepts (batch vs real-time, governance awareness).

Common traps

  • Confusing metrics vs parameters and what MLflow logs where.
  • Not recognizing data leakage and “too good to be true” evaluation results.
  • Treating “model registry” as a place for experiments (it’s for lifecycle/versions).

Readiness checklist

  • I can explain what MLflow tracks and why that matters for reproducibility.
  • I can choose metrics for classification vs regression and interpret them.
  • I can describe feature engineering steps that are safe from leakage.
  • I can explain what “registering a model” means (versions + stage).
  • I can describe batch vs real-time inference trade-offs at a high level.
  • Study Plan: 30/60/90 day schedules → Open
  • Syllabus: objectives by topic → Open
  • Cheatsheet: MLflow + feature engineering pickers → Open
  • Practice: drills and mixed sets → Start
Study Plan