Databricks Certified Machine Learning Associate Exam Prep

This section covers the core Databricks machine learning workflow for the Databricks Certified Machine Learning Associate exam. It emphasizes MLOps strategy, AutoML, Unity Catalog feature store usage, MLflow tracking and model registry, and model promotion practices.

• Identify the best practices of an MLOps strategy
• Best practices of an MLOps strategy
• Identify the advantages of using ML runtimes
• Advantages of using ML runtimes
• Identify how AutoML facilitates model/feature selection
• AutoML facilitates model/feature selection
• Identify the advantages AutoML brings to the model development process

Key references: ML Associate official exam guide · ExamPal shared topic tree

This section covers practical data preparation and exploratory analysis tasks in Spark. It includes summary statistics, outlier handling, visualization, feature comparison, missing-value imputation, one-hot encoding, and log-scale transformation decisions.

• 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 or median or mode value
• Impute missing values with the mode, mean, or median value
• Use one-hot encoding for categorical features

Key references: ML Associate official exam guide · ExamPal shared topic tree

Covers the practical skills needed to develop, tune, and evaluate machine learning models. This section emphasizes selecting appropriate algorithms, handling imbalanced data, building training pipelines, and using validation strategies and metrics to assess model performance.

• 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 or grid search or Bayesian search as a method for tuning hyperparameters
• Parallelize single node models for hyperparameter tuning

Key references: ML Associate official exam guide · ExamPal shared topic tree

Covers how to deploy machine learning models for batch, realtime, and streaming inference. It also includes using pandas for batch inference, Delta Live Tables for streaming inference, and deploying/querying models for realtime inference, including splitting data between endpoints for realtime interference.

• 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 interference

Key references: ML Associate official exam guide · ExamPal shared topic tree