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Databricks Certified Machine Learning Associate

Databricks ML Associate Practice Test

Start today's 10-question Databricks ML Associate set with source-backed explanations, local progress, and a fresh rotation every morning.

10 daily web questions Source-backed explanations 7-day score history Questions updated at May 28, 2026, 8:24 AM CDT
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Databricks ML Associate

Databricks Certified Machine Learning Associate

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Today's 10 Databricks ML Associate questions

Use this Databricks ML Associate practice test to review Databricks Certified Machine Learning Associate. Questions rotate daily and each explanation links to the source used to validate the answer.

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120 verified questions are in the live bank. Today’s focused 10-question set includes source-backed explanations.

Question 1 of 10
Objective DBML-04 Model Development

A scenario in Model Development depends on this detail: Imbalance-mitigation techniques such as cost-sensitive learning address the minority-class problem more directly than just adding more majority examples. Which option should the candidate choose?

Concept tested: Model Development (DBML-04)
Question 2 of 10
Objective DBML-06 Model Deployment

A team has deployed a model version to production, but they want to deploy a new 'challenger' version and route only 10% of the live production traffic to it to compare its real-world performance before making a full cutover. How can the team configure this traffic routing on their Databricks Model Serving endpoint?

Concept tested: Model Deployment (DBML-06)
Question 3 of 10
Objective DBML-05 Model Registry and Governance

A developer wants to register a custom Python model that was trained outside of Databricks (e.g., on a local laptop) into Unity Catalog so that it can be governed centrally. How can the developer accomplish this using the MLflow API?

Concept tested: Model Registry and Governance (DBML-05)
Question 4 of 10
Objective DBML-02 MLflow and Experiment Tracking

A team of data scientists wants to track model training runs for a project. They want to ensure all runs are organized under a single, central experiment in Databricks rather than being scattered across individual users' personal workspaces. How can the team programmatically set the active MLflow experiment to a specific path in the workspace?

Concept tested: MLflow and Experiment Tracking (DBML-02)
Question 5 of 10
Objective DBML-03 Data Processing

A data scientist wants a quick overview of count, mean, standard deviation, and quantile-style summaries for a Spark DataFrame. Which basic approach matches the exam objectives?

Concept tested: Data Processing (DBML-03)
Question 6 of 10
Objective DBML-01 Databricks Machine Learning Foundations

An engineer is reviewing Databricks Machine Learning Foundations for the Databricks ML Associate exam and a production task involving FeatureEngineeringClient. Which choice aligns with the cited source?

Concept tested: Databricks Machine Learning Foundations (DBML-01)
Question 7 of 10
Objective DBML-04 Model Development

An engineer wants to optimize hyperparameters for a machine learning model using Hyperopt in Databricks. They want to distribute the hyperparameter evaluations across multiple worker nodes of the Spark cluster to speed up the process. Which Hyperopt class or parameter should the engineer use to enable distributed search?

Concept tested: Model Development (DBML-04)
Question 8 of 10
Objective DBML-06 Model Deployment

An ML engineer is building a streaming data pipeline using Delta Live Tables (DLT). The pipeline receives continuous clickstream event streams, and they want to score these events in real-time as they flow through the pipeline using a registered MLflow model. What is the recommended approach to execute this streaming inference inside a DLT pipeline?

Concept tested: Model Deployment (DBML-06)
Question 9 of 10
Objective DBML-05 Model Registry and Governance

A team wants the deployment pipeline to always pull the current production-approved model version without hard-coding a version number. Which Unity Catalog feature is the best fit?

Concept tested: Model Registry and Governance (DBML-05)
Question 10 of 10
Objective DBML-02 MLflow and Experiment Tracking

An engineer wants to query the MLflow Tracking service programmatically to find the run that achieved the lowest validation loss for a specific experiment ID. Which Python code segment should the engineer use to retrieve this run?

Concept tested: MLflow and Experiment Tracking (DBML-02)
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Question 1 Databricks Machine Learning Foundations Databricks Machine Learning Foundations (DBML-01)
Question 2 Databricks Machine Learning Foundations Databricks Machine Learning Foundations (DBML-01)
Question 3 Databricks Machine Learning Foundations Databricks Machine Learning Foundations (DBML-01)
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