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

Exam snapshot (high level)

• Certification: Databricks Certified Machine Learning Professional (ML‑PRO)
• Audience: ML engineers and platform teams operating ML in production on Databricks
• Skills level: you should be comfortable with MLflow/registry and production concerns (governance, deployment, monitoring)
• 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‑PRO measures (what you should be able to do)

1) Build reliable and governed feature pipelines

• Feature definitions, training/serving consistency, and reuse across teams.
• Prevent leakage and enforce consistent preprocessing.

2) Manage model lifecycle end-to-end

• Reproducible training runs, registry versioning, and stage-based promotion.
• Auditability: trace model versions back to data and code.

3) Deploy models safely

• Batch scoring vs online serving trade-offs.
• Rollout/rollback thinking and risk controls.

4) Monitor and maintain production ML

• Performance drift, data drift, and operational telemetry.
• Triggering retraining vs remediation vs rollback decisions.

5) Apply platform governance

• Access control, lineage, and controlled promotion workflows.

Common traps

• Treating MLflow tracking as “enough” without controlled registry and promotion.
• Feature leakage and training/serving skew.
• Deploying without monitoring/rollback strategy.

Readiness checklist

• I can explain why feature pipelines must match training and serving transforms.
• I can explain MLflow runs vs registry versions and why both exist.
• I can choose batch vs online deployment based on latency and throughput needs.
• I can describe drift types and what actions are appropriate (retrain vs rollback).
• I can explain why governance and access control matter for production ML.