Databricks Machine Learning Associate: Skills Measured

Notebook workflow skills include reading code cells, understanding execution order, documenting steps, and connecting data preparation to model training. Candidates should know that notebooks are useful for exploration and collaboration, but reliable ML work still needs tracked runs, reusable logic, and clear handoff to deployment.

Spark data preparation involves selecting, filtering, joining, aggregating, and transforming data at scale. ML candidates should understand how prepared datasets feed training and evaluation. Common mistakes include training on poorly cleaned data, leaking target information, or using transformations during training that are not repeated during serving.

Feature engineering turns raw data into inputs the model can use. Candidates should recognize missing-value handling, categorical encoding, scaling, derived features, train-test splits, and feature reuse. Good feature work supports both model quality and consistency between training and inference.

MLflow tracking records experiments, runs, parameters, metrics, artifacts, and models. Candidates should understand how tracking supports experiment comparison and reproducibility. A scenario may ask where to find a run metric, how to compare model versions, or why an artifact was logged during development.

Model evaluation skills include comparing metrics across runs and choosing a model based on the problem. Classification, regression, and ranking tasks need different evaluation thinking. A candidate should not choose a model only because a metric is higher without understanding what the metric measures and whether the model fits the use case.

The model registry supports lifecycle management for models, while Unity Catalog supports governance across data and ML assets. Candidates should understand permissions, lineage, discoverability, and collaboration. Governance helps teams know which asset is trusted, who can use it, and how it relates to the training data or features.

AutoML can generate candidate models and baseline results, but users still need to interpret metrics, generated notebooks, selected features, and limitations. AutoML is not a replacement for understanding the data or deployment requirements. It is a tool for accelerating exploration and comparison.

Feature serving supports online retrieval of features for low-latency applications. Candidates should understand why production models need the same feature logic used during development. Feature serving questions often test training-serving consistency, endpoint use cases, and how features are made available for real-time prediction workflows.

Deployment decisions compare batch inference, online serving, endpoint traffic, monitoring, and rollback needs. The right choice depends on prediction latency, scale, update frequency, feature availability, and operational risk. Model deployment is part of the lifecycle, not a separate afterthought.

Original study examples may ask which MLflow object stores run metrics, when feature serving is appropriate, how to compare model runs, why Unity Catalog matters, or which deployment pattern fits a scoring workflow. These are study patterns, not official exam items or invented objectives.