Databricks ML Associate resources

The Databricks Machine Learning section carries 38% of this exam — more than model development at 31%. That balance surprises people who prepare as though it were a general machine learning exam. It is a platform exam: tracking, registries, feature stores and reproducibility matter as much as modelling.

Start with the official material

The exam guide and its weights: Databricks Machine Learning 38%, model development 31%, ML workflows 19%, model deployment 12%.

Databricks Academy’s machine learning path, free and self-paced.

The MLflow documentation specifically. Experiment tracking, the model registry and environment capture recur throughout the exam, and the documentation is more precise than summaries.

Practise on the platform

• Log a run properly: parameters, metrics, artefacts and the environment specification. Then try to reproduce it a week later and find what you forgot. Library versions are usually the answer.
• Record the training data’s table version with the run, not just the row count, and confirm you can reconstruct the dataset.
• Register a model and move it through stages, so the difference between a file in a folder and a registry entry with an approval trail is concrete.
• Use a feature store for one feature consumed by both training and serving, which is the direct remedy for training-serving skew.
• Fit an encoder during training and persist it for reuse at serving. Then re-fit at serving time deliberately and watch the predictions change.
• Split a time series randomly, see the optimistic score, then split chronologically and see the honest one.

That last exercise teaches leakage better than any explanation.

The passing score problem

Databricks publishes no passing score for this exam. Any number quoted elsewhere is a guess. That makes a weighted practice test the only calibration available — the free 20-question test here matches the published sections.

Worth paying for

A practice test with explanations.

Employer-funded training, if offered.

What to skip

Deep algorithm theory. You will be asked whether recall or precision matters when missing a positive case is costly, not to derive an optimiser.

Braindumps.

Professional-level material. The ML Professional exam assumes production experience and studying it first is inefficient.

Generic machine learning courses. They will cover the 31% model development section and miss the 38% platform section entirely, which is the most common preparation mistake for this exam.

A sensible order

1. Read the exam guide and note that the platform section is the largest.
2. Work through the Academy machine learning path.
3. Practise the six exercises above, particularly reproducibility and the time series split.
4. Take the practice test.
5. Return to the Academy module or documentation for the weakest section.

Where this sits

The Databricks certification path explains the lanes, and why six to twelve months of production work belongs between this exam and the professional one.