Free Certification Practice Questions

Validates foundational machine learning skills on the Databricks Data Intelligence Platform. Covers Databricks ML capabilities including AutoML, Unity Catalog for ML governance, and core MLflow features such as experiment tracking, the Model Registry, and model serving. Assesses exploratory data analysis, feature engineering, model training, hyperparameter tuning, model evaluation and selection, and model deployment patterns. All machine learning code in this exam is in Python; data manipulation tasks may use SQL.

Assesses the ability to design, implement, and manage enterprise-scale machine learning solutions using advanced Databricks platform capabilities. Covers building scalable ML pipelines with SparkML, implementing distributed training and hyperparameter tuning, leveraging advanced MLflow features, and utilizing Feature Store concepts for automated feature pipelines. Evaluates MLOps practices including testing strategies, environment management with Databricks Asset Bundles, automated retraining workflows, and monitoring using Lakehouse Monitoring for drift detection. Assesses deployment strategies, custom model serving, and model rollout management. All machine learning code in this exam is in Python.