200+ Free Databricks Machine Learning Associate Practice Questions

Pass your Databricks Certified Machine Learning Associate exam on the first try — instant access, no signup required.

✓ No registration✓ No credit card✓ No hidden fees✓ Start practicing immediately

200+ Questions

100% Free

1 / 200

Question 1

Score: 0/0

A team wants a repeatable path from experimentation to production on Databricks. Which setup best reflects a sound MLOps strategy?

Separate development, staging, and production environments with versioned code and tracked model artifacts

One shared workspace where notebooks are edited directly in production to keep changes fast

Manual model file copies between users to avoid registry overhead

Training new models whenever a stakeholder asks, without keeping prior run metadata

to track

2026 Statistics

Key Facts: Databricks Machine Learning Associate Exam

Scored Questions

Official exam page

90 min

Time Limit

Official exam page

$200

Exam Fee

Official exam page

38%

Largest Domain

Databricks Machine Learning

Not public

Passing Score

Current official materials

2 years

Validity

Official exam page

Mar 1, 2025

Live Guide Version

Official exam guide

As of March 10, 2026, Databricks publicly lists 48 scored questions, a 90-minute time limit, a $200 registration fee, and four weighted domains: Databricks Machine Learning at 38%, ML Workflows at 19%, Model Development at 31%, and Model Deployment at 12%. Databricks does not publicly publish an exam-specific passing score on the current exam page or live exam guide. The current public guide linked by Databricks is versioned March 1, 2025, and no newer public 2026 blueprint revision was posted on the official exam page when this content was updated.

Sample Databricks Machine Learning Associate Practice Questions

Try these sample questions to test your Databricks Machine Learning Associate exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 200+ question experience with AI tutoring.

1A team wants a repeatable path from experimentation to production on Databricks. Which setup best reflects a sound MLOps strategy?

A.Separate development, staging, and production environments with versioned code and tracked model artifacts

B.One shared workspace where notebooks are edited directly in production to keep changes fast

C.Manual model file copies between users to avoid registry overhead

D.Training new models whenever a stakeholder asks, without keeping prior run metadata

Explanation: A strong MLOps strategy emphasizes reproducibility, controlled promotion, and traceability. Separate environments plus versioned code and tracked artifacts make it possible to test safely before promoting changes.

2Which practice most directly improves reproducibility in a Databricks MLOps workflow?

A.Keeping training code in source control and logging run parameters and artifacts with MLflow

B.Relying on notebook revision history alone and skipping experiment tracking

C.Letting each data scientist rename columns locally before training

D.Saving only the final predictions because metrics can be recalculated later

Explanation: Source control captures code changes, while MLflow records the exact parameters, metrics, and artifacts used for a run. Together they let a team reproduce or audit a model result later.

3What is a primary advantage of using a Databricks Runtime for Machine Learning cluster instead of a general-purpose runtime?

A.It comes with common ML libraries and integrations preinstalled

B.It prevents all package version conflicts automatically

C.It removes the need to manage cluster permissions

D.It allows models to skip feature engineering entirely

Explanation: Machine Learning runtimes reduce setup time because common libraries such as scikit-learn, MLflow integrations, and other ML tooling are already included. That makes experimentation faster and more consistent across users.

4A data scientist needs to start prototyping quickly on Databricks with fewer library-installation steps and more predictable package compatibility. What is the best choice?

A.A standard SQL warehouse

B.A Databricks Runtime for Machine Learning cluster

C.A job cluster running the smallest non-ML runtime

D.A dashboard with Genie enabled

Explanation: Databricks Runtime for Machine Learning is designed for ML development workflows and bundles the libraries typically needed for training and tracking. It reduces setup friction compared with starting from a general-purpose runtime.

5How does Databricks AutoML help with model selection for a supervised tabular problem?

A.It tries multiple algorithms and ranks resulting runs using a selected evaluation metric

B.It always trains only logistic regression first, then stops

C.It requires the user to hand-code every candidate model notebook before starting

D.It chooses features only by alphabetic order

Explanation: AutoML automates the comparison of multiple candidate models and records the results so the user can review ranked runs. This provides a fast baseline and helps identify promising model families.

6Which AutoML output is especially useful when a team wants to review and customize the winning approach after the automated run finishes?

A.Only a screenshot of the leaderboard

B.Generated notebooks for the data exploration and best trial

C.A fixed binary model file with no source code

D.A cluster event log

Explanation: Databricks AutoML generates notebooks that show the underlying preprocessing and training logic. Those notebooks let a team reproduce the winning trial and adapt it for more customized development.

7A business analyst has a labeled customer-churn table and wants a strong baseline model quickly before any custom tuning. Which approach is most appropriate?

A.Start with AutoML on the tabular dataset

B.Build a custom deep neural network from scratch immediately

C.Deploy a model serving endpoint before training

D.Create an online feature store first and skip model development

Explanation: AutoML is a good first step for labeled tabular problems because it can quickly compare candidate algorithms and produce an initial benchmark. The team can then refine the best approach if needed.

8When is Databricks AutoML less suitable than a fully custom training workflow?

A.When the user wants a fast baseline on structured data

B.When the team must implement a highly customized deep learning architecture and training loop

C.When the dataset already contains a target column

D.When multiple candidate models should be compared

Explanation: AutoML is strong for baseline model development, especially on standard structured tasks. Highly customized deep learning architectures and training logic usually require a manual workflow.

9What is a key benefit of creating feature tables in Unity Catalog instead of keeping them only at the workspace level?

A.They can be governed and discovered across multiple workspaces at the account level

B.They no longer need primary keys

C.They can be queried only by the original author, which improves security

D.They automatically become online stores

Explanation: Unity Catalog provides centralized governance, discoverability, and cross-workspace access patterns. That makes shared feature reuse easier than isolating features inside a single workspace.

10In a Unity Catalog-enabled workspace, which Python client is used to create a new feature table in Unity Catalog?

A.FeatureEngineeringClient

B.CrossValidator

C.MlflowClient

D.SparkTrials

Explanation: FeatureEngineeringClient is the Unity Catalog-oriented client for Databricks feature engineering workflows. It includes APIs such as create_table and write_table for feature table management.

About the Databricks Machine Learning Associate Exam

The Databricks Certified Machine Learning Associate exam validates foundational machine learning work on Databricks, including platform-aware MLOps decisions, feature engineering, MLflow and Unity Catalog usage, model development, and deployment choices across batch, realtime, and streaming patterns.

Assessment

48 scored questions; unscored items may appear

Time Limit

90 minutes

Passing Score

Not publicly published by Databricks

Exam Fee

$200 (Databricks / Kryterion Webassessor)

Databricks Machine Learning Associate Exam Content Outline

38%

Databricks Machine Learning

MLOps strategy, ML runtimes, AutoML, Unity Catalog feature engineering, online versus offline features, MLflow tracking, model registration, and promotion patterns.

19%

ML Workflows

Data exploration, summary statistics, visualizations, outlier handling, missing-value imputation, categorical encoding, and transformations used before training.

31%

Model Development

Algorithm selection, class imbalance mitigation, estimators versus transformers, pipelines, hyperparameter tuning, cross-validation, evaluation metrics, and bias-variance tradeoffs.

12%

Model Deployment

Choosing between batch, realtime, and streaming inference; deploying custom models; using pandas for batch scoring; Delta Live Tables inference; and controlled endpoint rollout.

How to Pass the Databricks Machine Learning Associate Exam

What You Need to Know

Passing score: Not publicly published by Databricks
Assessment: 48 scored questions; unscored items may appear
Time limit: 90 minutes
Exam fee: $200

Keys to Passing

Complete 500+ practice questions
Score 80%+ consistently before scheduling
Focus on highest-weighted sections
Use our AI tutor for tough concepts

Databricks Machine Learning Associate Study Tips from Top Performers

1Study by official weight: spend the most time on Databricks Machine Learning and Model Development because those two domains account for 69% of the exam.

2Practice with Unity Catalog feature engineering and MLflow together so you can reason through feature lookup, experiment tracking, registration, and promotion as one workflow.

3Be able to choose metrics based on the business objective, especially when class imbalance or log-transformed targets make naive accuracy or raw prediction interpretation misleading.

4Use pipelines and tuning tools in realistic scenarios so cross-validation, Hyperopt, and search-space reasoning feel procedural instead of theoretical.

5Know when batch, realtime, or streaming inference is the right serving pattern, and what Databricks tooling supports each option.

6Re-check the official exam page and guide shortly before scheduling, because Databricks states that exam guides can be updated when blueprint changes take effect.

Frequently Asked Questions

How many questions are on the Databricks Certified Machine Learning Associate exam?

Databricks' current exam page lists 48 scored questions with a 90-minute time limit and a $200 registration fee. The live exam guide also notes that unscored items may appear, with additional time factored into the appointment.

What are the official domain weights?

The current Databricks exam page lists four weighted domains: Databricks Machine Learning at 38%, ML Workflows at 19%, Model Development at 31%, and Model Deployment at 12%. That means platform-specific ML tooling and model-development judgment should drive most of your study time.

What passing score do I need?

Databricks does not publicly publish a fixed passing score for this exam on the current exam page or the live March 1, 2025 exam guide. Older community references sometimes cite 70%, but that benchmark is not stated on the current official ML Associate exam materials.

Were there any 2026 blueprint changes?

No newer public 2026 exam-guide revision was found on the official Databricks site when this content was updated on March 10, 2026. The current public guide linked from the exam page is still the version marked as covering the live exam as of March 1, 2025.

Do I need hands-on Databricks experience before taking the exam?

Databricks lists no formal prerequisite, but it recommends related training and at least 6 months of hands-on experience performing the machine learning tasks in the guide. In practice, the exam is much easier if you have actually used AutoML, MLflow, feature tables, tuning workflows, and model serving.

Which Databricks features should I know best?

Focus on AutoML, MLflow tracking and model registration, Unity Catalog feature engineering, Spark or pandas-based preprocessing workflows, hyperparameter tuning, evaluation metrics, and the tradeoffs between batch, realtime, and streaming deployment patterns. The exam expects tool selection and workflow judgment, not just memorized definitions.