Databricks ML Associate study plan

Five weeks at 6–8 hours a week, assuming roughly the 6+ months of hands-on experience Databricks recommends. You need a Databricks workspace — the largest section is the platform itself, and it cannot be learned from reading.

The plan follows the weighting. Databricks Machine Learning is 38%, Model Development 31%, ML Workflows 19%, Model Deployment 12%.

Weeks 1 and 2: Databricks Machine Learning (38%)

The largest section, and the one people wrongly treat as background. Two weeks.

Week 1 — the ML workspace and MLflow:

• The Databricks ML runtime and clusters configured for ML work.
• MLflow tracking: runs, parameters, metrics, artefacts. Logging an experiment and comparing runs.
• MLflow model registry: registering a model, versions, stages, and promotion.
• Notebooks and repos as the working surface.

Week 2 — AutoML and Unity Catalog:

• AutoML: running an experiment, what it produces, and — importantly — that it generates editable notebooks rather than a black box.
• When AutoML is the right starting point and when it is not.
• Unity Catalog for ML: governing models and data, permissions, and lineage.
• Feature management and reuse across training and inference.

Week 3: model development (31%)

• Exploratory data analysis and preparing data for modelling.
• Feature engineering: encoding, scaling, handling missing values.
• Training with common libraries, and distributed training considerations.
• Hyperparameter tuning, including automated search.
• Evaluation metrics and choosing the right one: precision and recall over accuracy on imbalanced data, and the regression equivalents.
• Overfitting and underfitting, and how they present.

Week 4: ML workflows (19%) and model deployment (12%)

Workflows:

• Structuring an end-to-end ML process from data to registered model.
• Scheduling and automating jobs.
• Reproducibility: which data, which code, which parameters produced this model.

Deployment:

• Batch versus real-time inference, and which a requirement calls for.
• Serving a registered model.
• Basic monitoring after deployment.

Only about six questions come from deployment. Cover it properly, then stop.

Week 5: practice and repair

• Full practice exam under real conditions: 90 minutes, 48 questions.
• Sort mistakes by section. If the platform section is weak, that is 38% of the exam and the priority.
• Rebuild the weakest area, then take a second practice exam.

Where the hours go

The mistake to avoid

Treating this as a machine learning theory exam.

It is not. The largest section is the platform, and the questions test whether you can operate MLflow, AutoML and Unity Catalog competently. A candidate with strong ML theory and no MLflow experience will struggle; a candidate who uses Databricks daily with moderate ML knowledge will generally pass.

If you have limited time, spend it in the workspace, not in a textbook.

One project that covers most of it

Take a dataset and run it through the full cycle: explore it, engineer a couple of features, run an AutoML experiment, look at the notebooks it generates, train a model manually and compare, log both runs to MLflow, tune hyperparameters, register the better model, govern it in Unity Catalog, and deploy it for batch inference.

That single sequence touches all four sections and most of the platform features the exam names.

Read Python ML code out loud as you study. The exam shows you code and asks what it does — recognition speed matters more than being able to write it.