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Databricks Certified Machine Learning Associate

Databricks ML Associate Practice Test

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Questions updated at Jul 10, 2026, 12:01 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.

Today’s Set
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150 verified questions are in the live bank. Free daily questions are selected from a rotating sample set. Unlock Pro to access the full question bank.

Question 1 of 10
Objective DBML-05 Model Registry and Governance

For Unity Catalog model management, models must be governed, audited, traced, and reused across workspaces. What does Unity Catalog add?

Concept tested: Model Registry and Governance (DBML-05)
Source:Manage model lifecycle in Unity Catalog
Question 2 of 10
Objective DBML-01 Databricks Machine Learning Foundations

A Python feature engineering workflow needs to create and write a governed feature table in Databricks. Which client should the engineer use?

Concept tested: Databricks Machine Learning Foundations (DBML-01)
Question 3 of 10
Objective DBML-02 MLflow and Experiment Tracking

A data scientist has trained a scikit-learn model and wants to register it in MLflow for later use. Which MLflow call should they use to log the model as an artifact?

Concept tested: MLflow and Experiment Tracking (DBML-02)
Question 4 of 10
Objective DBML-04 Model Development

A classifier is being evaluated on class labels, and it's critical to assess performance on a minority class. Which metric family is most appropriate?

Concept tested: Model Development (DBML-04)
Question 5 of 10
Objective DBML-03 Data Processing

A categorical feature has thousands of distinct values. What is the main risk of one-hot encoding it directly?

Concept tested: Data Processing (DBML-03)
Question 6 of 10
Objective DBML-06 Model Deployment

An application needs low-latency predictions from a deployed model endpoint. Which inference mode fits?

Concept tested: Model Deployment (DBML-06)
Question 7 of 10
Objective DBML-05 Model Registry and Governance

During registry migration, mL assets need a governed catalog layer for permissions, discovery, and lineage. Which Databricks service provides it?

Concept tested: Model Registry and Governance (DBML-05)
Question 8 of 10
Objective DBML-01 Databricks Machine Learning Foundations

A deployed model needs fresh feature values at request time from a low-latency lookup path. Which Databricks serving pattern should be used?

Concept tested: Databricks Machine Learning Foundations (DBML-01)
Question 9 of 10
Objective DBML-02 MLflow and Experiment Tracking

For run comparison, several tracked runs must be compared programmatically by validation metric. What MLflow approach fits?

Concept tested: MLflow and Experiment Tracking (DBML-02)
Question 10 of 10
Objective DBML-04 Model Development

A classifier is performing poorly on a rare positive class. What training approach can reduce the class-imbalance problem?

Concept tested: Model Development (DBML-04)
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Official exam resources

Use these official Databricks resources alongside the daily practice set. They cover the provider's own exam page, study guide, or prep material.

Certification Databricks Machine Learning Associate certification Docs MLflow documentation

Need adjacent Databricks practice pages too? Databricks practice hub.

Source-backed answer review

The free daily Databricks ML Associate set includes crawlable question text, answer choices, correct answer labels, objective mapping, and source links. Only the first SEO card includes answer explanations. Pro-only bank questions stay locked; this section mirrors only the 10 free daily questions already shown on this page.

Question 1 For Unity Catalog model management, models must be governed, audited, traced, and reused across workspaces. What does Unity Catalog add?

Answer choices

  1. A. Unity Catalog centralizes governance, auditing, lineage, and cross-workspace model access
  2. B. Workspace-local notebook folders
  3. C. A single-purpose serving endpoint
  4. D. Cluster driver logs

Correct answer

Unity Catalog centralizes governance, auditing, lineage, and cross-workspace model access

Unity Catalog provides centralized governance for ML assets, including permissions, lineage, discovery, and cross-workspace access. That makes it the right layer for governed features and models.

Wrong-answer review

  • B. Workspace-local notebook folders: Workspace-local notebook folders do not provide centralized cross-workspace governance or lineage.
  • C. A single-purpose serving endpoint: A serving endpoint exposes predictions; it is not the catalog governance layer.
  • D. Cluster driver logs: Cluster driver logs are runtime logs and are not a governed catalog for ML assets.

Objective/domain: Model Registry and Governance (DBML-05)

Source: Manage model lifecycle in Unity Catalog

Question 2 A Python feature engineering workflow needs to create and write a governed feature table in Databricks. Which client should the engineer use?

Answer choices

  1. A. MlflowClient
  2. B. dbutils.secrets
  3. C. ModelServingClient
  4. D. FeatureEngineeringClient

Correct answer

FeatureEngineeringClient

Objective/domain: Databricks Machine Learning Foundations (DBML-01)

Source: Feature tables

Question 3 A data scientist has trained a scikit-learn model and wants to register it in MLflow for later use. Which MLflow call should they use to log the model as an artifact?

Answer choices

  1. A. mlflow.log_artifact(local_path=model, artifact_path="model")
  2. B. mlflow.sklearn.save_model(model, path="dbfs:/models")
  3. C. mlflow.set_tag("model", model)
  4. D. mlflow.sklearn.log_model(sk_model=model, artifact_path="model")

Correct answer

mlflow.sklearn.log_model(sk_model=model, artifact_path="model")

Objective/domain: MLflow and Experiment Tracking (DBML-02)

Source: Log, load, and register MLflow Models

Question 4 A classifier is being evaluated on class labels, and it's critical to assess performance on a minority class. Which metric family is most appropriate?

Answer choices

  1. A. Mean Squared Error
  2. B. A classification metric such as F1 or ROC/AUC
  3. C. Raw accuracy only
  4. D. R-squared

Correct answer

A classification metric such as F1 or ROC/AUC

Objective/domain: Model Development (DBML-04)

Source: Databricks Certified Machine Learning Associate - Exam Guide

Question 5 A categorical feature has thousands of distinct values. What is the main risk of one-hot encoding it directly?

Answer choices

  1. A. It automatically removes all missing values
  2. B. It forces the target variable to become categorical
  3. C. It prevents numeric feature scaling elsewhere in the pipeline
  4. D. High-dimensional sparse features with compute and overfitting risk

Correct answer

High-dimensional sparse features with compute and overfitting risk

Objective/domain: Data Processing (DBML-03)

Source: OneHotEncoder in Spark ML

Question 6 An application needs low-latency predictions from a deployed model endpoint. Which inference mode fits?

Answer choices

  1. A. Batch inference on a schedule
  2. B. Offline feature table backfill
  3. C. MLflow experiment comparison
  4. D. Realtime model serving

Correct answer

Realtime model serving

Objective/domain: Model Deployment (DBML-06)

Source: Databricks Certified Machine Learning Associate - Exam Guide

Question 7 During registry migration, mL assets need a governed catalog layer for permissions, discovery, and lineage. Which Databricks service provides it?

Answer choices

  1. A. Workspace-local notebook folders
  2. B. A single-purpose serving endpoint
  3. C. Unity Catalog
  4. D. Cluster driver logs

Correct answer

Unity Catalog

Objective/domain: Model Registry and Governance (DBML-05)

Source: What is Unity Catalog?

Question 8 A deployed model needs fresh feature values at request time from a low-latency lookup path. Which Databricks serving pattern should be used?

Answer choices

  1. A. Run a batch notebook against the offline table before each request
  2. B. Feature Serving or model serving integrated with feature lookup
  3. C. Store all historical feature rows inside the model artifact
  4. D. Use a SQL dashboard query as the prediction path

Correct answer

Feature Serving or model serving integrated with feature lookup

Objective/domain: Databricks Machine Learning Foundations (DBML-01)

Source: Feature Serving endpoints

Question 9 For run comparison, several tracked runs must be compared programmatically by validation metric. What MLflow approach fits?

Answer choices

  1. A. Use the MLflow Client API to compare runs and select the best one by the target metric
  2. B. Rename the notebook after the preferred run
  3. C. Manually delete weaker runs before comparing metrics
  4. D. Use Unity Catalog privileges to infer the best run

Correct answer

Use the MLflow Client API to compare runs and select the best one by the target metric

Objective/domain: MLflow and Experiment Tracking (DBML-02)

Source: Databricks Certified Machine Learning Associate - Exam Guide

Question 10 A classifier is performing poorly on a rare positive class. What training approach can reduce the class-imbalance problem?

Answer choices

  1. A. Optimize only raw accuracy
  2. B. Use cost-sensitive learning or other imbalance-mitigation techniques during training
  3. C. Drop all majority-class examples
  4. D. Use a regression metric such as RMSE

Correct answer

Use cost-sensitive learning or other imbalance-mitigation techniques during training

Objective/domain: Model Development (DBML-04)

Source: Databricks Certified Machine Learning Associate - Exam Guide

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