Machine Learning Associate
The associate-level certification for using Databricks to perform core machine learning tasks, from feature engineering and AutoML to MLflow tracking, model registration in Unity Catalog, and endpoint deployment.
This exam guide does not publish per-domain weights, so the sections below are in the guide's own order and carry no percentage.
The exam still asks in the old words. The product answers in the new ones. Left is what you will read in the question; right is what you will click in the workspace.
Databricks Machine Learning
Exam guide objectives
- Identify the best practices of an MLOps strategy
- Identify the advantages of using ML runtimes
- Identify how AutoML facilitates model and feature selection
- Identify the advantages AutoML brings to the model development process
- Identify the benefits of creating feature store tables at the account level in Unity Catalog versus the workspace level
- Create a feature store table in Unity Catalog
- Write data to a feature store table
- Train a model with features from a feature store table
- Score a model using features from a feature store table
- Describe the differences between online and offline feature tables
- Identify the best run using the MLflow Client API
- Manually log metrics, artifacts, and models in an MLflow run
- Identify information available in the MLflow UI
- Register a model using the MLflow Client API in the Unity Catalog registry
- Identify benefits of registering models in the Unity Catalog registry over the workspace registry
- Identify scenarios where promoting code is preferred over promoting models, and vice versa
- Set or remove a tag for a model
- Promote a challenger model to a champion model using aliases
Data Processing
Exam guide objectives
- Compute summary statistics on a Spark DataFrame using .summary() or dbutils data summaries
- Remove outliers from a Spark DataFrame based on standard deviation or IQR
- Create visualizations for categorical or continuous features
- Compare two categorical or two continuous features using the appropriate method
- Compare and contrast imputing missing values with the mean, median, or mode value
- Impute missing values with the mode, mean, or median value
- Use one-hot encoding for categorical features
- Identify the model types or data sets for which one-hot encoding is or is not appropriate
- Identify scenarios where log scale transformation is appropriate
Model Development
Exam guide objectives
- Use ML foundations to select the appropriate algorithm for a given model scenario
- Identify methods to mitigate data imbalance in training data
- Compare estimators and transformers
- Develop a training pipeline
- Use Hyperopt's fmin operation to tune a model's hyperparameters
- Perform random, grid, or Bayesian search as a method for tuning hyperparameters
- Parallelize single-node models for hyperparameter tuning
- Describe the benefits and downsides of cross-validation over a train-validation split
- Perform cross-validation as part of model fitting
- Identify the number of models trained in conjunction with a grid-search and cross-validation process
- Use common classification metrics: F1, log loss, ROC/AUC, etc.
- Use common regression metrics: RMSE, MAE, R-squared, etc.
- Choose the most appropriate metric for a given scenario objective
- Identify the need to exponentiate log-transformed variables before calculating evaluation metrics or interpreting predictions
- Assess the impact of model complexity and the bias-variance tradeoff on model performance
Model Deployment
Exam guide objectives
- Identify the differences and advantages of model serving approaches: batch, realtime, and streaming
- Deploy a custom model to a model endpoint
- Use pandas to perform batch inference
- Identify how streaming inference is performed with Delta Live Tables
- Deploy and query a model for realtime inference
- Split data between endpoints for realtime inference
How to use this roadmap
The four domains follow the order of the official exam guide (live as of March 1, 2025). This version does not publish a percentage weight per section, so treat every domain as equally likely and use the number of objectives, not a published weight, as a rough guide to depth.
What makes this exam different from the data engineering tracks: it is scoped to a single machine learning workflow, run mostly through scikit-learn, SparkML, and MLflow, with SQL used only for general data manipulation, not for the ML-specific tasks. Model Development is the domain with the least coverage in this vault today: hyperparameter tuning with Hyperopt, cross-validation, and classification and regression metrics are exam topics without a concept page yet, so pair this roadmap with the official self-paced courses for that section.