**Updated Nov/2025: Addtional Practice Test 3 & Additional  Questions added in PT2

**Quality Check Done | Nov 2025

**Reviewed Oct/2025

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Preparing for the Databricks Machine Learning Associate Certification?
This course gives you the most accurate, updated, and exam-focused practice tests to help you clear the certification with confidence.

Built from the latest 2025-2026 exam changes, these practice exams simulate the real Databricks testing experience — including scenario-based questions, ML workflows, Unity Catalog, Feature Engineering, AutoML, MLflow, Model Serving, and Spark ML fundamentals.

Each question is followed by a detailed explanation, helping you understand not only why the correct answer is right — but also why the other options are wrong. This ensures deeper learning and exam confidence.

Whether you are a data scientist, ML engineer, or beginner transitioning into Databricks Machine Learning, these practice exams give you the skill, speed, and clarity required to pass on the first attempt.

By the end of this course, you will be fully prepared for the Databricks Machine Learning Associate Certification, with complete coverage of all major exam domains.

What You’ll Learn

• Master every topic in the Databricks Machine Learning Associate exam
• Practice with updated and aligned exam questions
• Understand Databricks ML fundamentals used in real projects
• Learn MLflow tracking, registry, model deployment, and governance
• Confidently work with pandas API on Spark and Feature Engineering patterns
• Understand streaming ML, AutoML, Model Serving, and Unity Catalog
• Improve exam speed with timed practice tests
• Avoid common mistakes by reviewing detailed explanations
• Apply Databricks ML concepts to real-world workloads

Are There Any Course Requirements?

• Basic understanding of machine learning concepts
• Familiarity with Python or Spark is helpful (not mandatory)
• No Databricks workspace is required — this course focuses on exam preparation

Who This Course Is For

• Data Scientists preparing for the ML Associate exam
• ML Engineers working on Databricks platforms
• Developers transitioning to Spark ML or Databricks ML
• Professionals wanting to validate ML skills with a global certification
• Anyone seeking real exam-style practice tests

Course Includes

• Multiple full-length practice exams
• Detailed explanations for every answer
• Regular updates aligned with Databricks exam changes
• Scenario-based and real-world problem solving questions
• Lifetime access with continuous improvements

This exam covers:

1. Databricks Machine Learning – 38%

2. ML Workflows – 19%

3. Model Development – 31%

4. Model Deployment – 12%

Exam Outline

Section 1: Databricks Machine Learning

● Identify the best practices of an MLOps strategy

● Identify the advantages of using ML runtimes

● Identify how AutoML facilitates model/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 in

Databricks vs at 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

Section 2: Data Processing

● 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

● Identify and explain the model types or data sets for which one-hot encoding is or is not

appropriate.

● Identify scenarios where log scale transformation is appropriate

Section 3: Model Development

● 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

● Describe the benefits and downsides of using cross-validation over a train-validation split.

● Perform cross-validation as a part of model fitting.

● Identify the number of models being 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

Section 4: Model Deployment

● 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

Ready to Take the Next Step?

Enroll in this course today and equip yourself with the knowledge and practice you need to successfully earn your Databricks Certified Machine Learning Associate certification. Your future in machine learning awaits!

Who this course is for:

• Aspiring Databricks Certified Machine Learning Associates: If you’re aiming to earn this valuable certification and demonstrate your expertise in machine learning on the Databricks platform, this course will equip you with the knowledge and practice you need to succeed.
• Data Scientists and Machine Learning Engineers: Whether you’re new to Databricks or have some experience, this course will help you solidify your understanding of ML concepts in the Databricks context and prepare you for the certification exam.
• Data Professionals Transitioning to ML: If you’re a data analyst, data engineer, or other data professional looking to expand your skills into machine learning, this course will guide you through the essential concepts and tools used in Databricks for ML.
• Anyone Seeking to Validate Their ML Skills: Even if certification isn’t your immediate goal, this course offers a structured way to assess your current ML knowledge and identify areas for improvement on the Databricks platform.
• Professionals Seeking Career Advancement: The Databricks Certified Machine Learning Associate certification is a valuable asset in the job market. By completing this course and earning the certification, you’ll enhance your career prospects and demonstrate your commitment to professional growth.