Databricks Certified Machine Learning Associate
Practice exam for the Databricks Certified Machine Learning Associate certification. Covers Databricks ML platform, AutoML, MLflow tracking, Feature Store, model development with scikit-learn and PyTorch, and model deployment with Databricks Model Serving.
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Sample Questions — Databricks Certified Machine Learning Associate
5 free sample questions from this practice exam. Correct answers are highlighted.
1. A data scientist is setting up a new Databricks cluster for training scikit-learn and XGBoost models. They want to avoid manually installing these libraries. Which cluster configuration should they choose?
2. A data scientist is using MLflow to track experiments. After a training run completes, the team wants to understand exactly what information MLflow stores automatically in the tracking server. Which of the following is NOT stored by the MLflow tracking server during a run?
3. Which of the following libraries is pre-installed in Databricks Runtime ML but NOT in the standard Databricks Runtime?
4. A machine learning engineer needs to track multiple evaluation metrics over training epochs in MLflow. Which approach correctly logs a metric at each step?
5. A machine learning engineer needs to fine-tune a large language model using GPU-accelerated training on Databricks. Which action is required to enable GPU usage on a cluster?
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Study Guides & Articles
How to Pass Databricks Certified Machine Learning Associate in 2026: Study Guide
Complete study guide for the Databricks Machine Learning Associate exam. Covers MLflow experiment tracking, Databricks AutoML, Feature Store, and distributed ML with Spark.
Databricks ML Associate Deep Dive: MLflow Tracking, AutoML & Feature Store
Master the hardest Databricks ML Associate topics: MLflow experiment tracking API, model registry lifecycle, AutoML exploration, Feature Store APIs, and hyperparameter tuning.
Databricks ML Associate Exam Traps: MLflow, Registry & Deployment Gotchas
Avoid the most common Databricks ML Associate exam mistakes. Master MLflow API traps, model registry stage transitions, AutoML limitations, and Feature Store usage patterns.
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