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Databricks Certified Machine Learning Associate

Databricks ML Associate Practice Test

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Questions updated at Jul 18, 2026, 1:30 PM CDT

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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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Question 1 of 10
Objective DBML-04 Model Development

For model evaluation, a Spark ML transformer is applied to input data. What output should the engineer expect?

Concept tested:
Source:Databricks Certified Machine Learning Associate - Exam Guide
Question 2 of 10
Objective DBML-06 Model Deployment

A model endpoint should handle changing request volume and reduce idle compute when traffic drops. Which serving behavior provides this?

Concept tested:
Question 3 of 10
Objective DBML-01 Databricks Machine Learning Foundations

A team needs to quickly generate baseline models and training notebooks for ML projects with minimal manual setup. Which Databricks capability should they leverage?

Concept tested:
Question 4 of 10
Objective DBML-05 Model Registry and Governance

A logged MLflow model artifact should become a governed Unity Catalog model. Which registration flow fits?

Concept tested:
Question 5 of 10
Objective DBML-06 Model Deployment

A team wants served model requests and responses logged for monitoring. Which Databricks feature captures them?

Concept tested:
Question 6 of 10
Objective DBML-01 Databricks Machine Learning Foundations

For governed feature reuse, a feature computation requires Python logic registered within the Unity Catalog. What mechanism should be used to ensure this logic is executed on demand and governed by the catalog?

Concept tested:
Question 7 of 10
Objective DBML-03 Data Processing

Before model training, a notebook user wants quick descriptive statistics for DataFrame columns before modeling. What should they call?

Concept tested:
Question 8 of 10
Objective DBML-02 MLflow and Experiment Tracking

A data scientist is training a model using scikit-learn and wants to log the trained model as an MLflow artifact. Which MLflow call should they use to accomplish this?

Concept tested:
Question 9 of 10
Objective DBML-04 Model Development

A grid search is performed to optimize a model's hyperparameters. If the grid has three values for one hyperparameter and six values for another, what is the total number of model combinations evaluated?

Concept tested:
Question 10 of 10
Objective DBML-03 Data Processing

For exploratory data analysis, a nominal categorical field has no ordinal meaning and must become model-readable features. What encoding should be used?

Concept tested:
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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 and any extra learning features. Pro-only bank questions stay locked; this section mirrors only the 10 free daily questions already shown on this page.

Question 1 For model evaluation, a Spark ML transformer is applied to input data. What output should the engineer expect?

Answer choices

  1. A. A fitted model object that still needs transform()
  2. B. A Unity Catalog model alias
  3. C. It takes a DataFrame as input and returns a transformed DataFrame
  4. D. A scalar evaluation metric only

Correct answer

It takes a DataFrame as input and returns a transformed DataFrame

A Spark ML Transformer takes a DataFrame as input and returns another DataFrame with transformed or added columns.

Wrong-answer review

  • A. A fitted model object that still needs transform(): A fitted model object is produced by fitting an Estimator, not by applying a Transformer.
  • B. A Unity Catalog model alias: A model alias belongs to model management, not Spark ML transformation output.
  • D. A scalar evaluation metric only: A Transformer changes data rows; it does not return only a single evaluation metric.

Extra learning features

Why candidates miss this

The expectation that a Transformer produces a fitted model object reveals a misunderstanding of Spark ML pipelines. Transformers, unlike Estimators, don't require a `fit()` method; they directly transform data. A fitted model object is the result of running an Estimator, which is used to define the transformation logic, not execute it. Likely wrong answer: A fitted model object that still needs transform() Review focus: Spark ML Estimators vs. Transformers

Why this matters

Exam items may test it takes a dataframe as input and returns a transfo through dbml-04, especially how the cited concept changes the decision. A strong answer keeps that source detail separate from nearby distractors.

Objective/domain: Model Development

Source: Databricks Certified Machine Learning Associate - Exam Guide

Question 2 A model endpoint should handle changing request volume and reduce idle compute when traffic drops. Which serving behavior provides this?

Answer choices

  1. A. A fixed-size classic cluster behind the endpoint
  2. B. A custom cron job that resizes clusters every few minutes
  3. C. Serverless Model Serving autoscaling, including scale-to-zero
  4. D. Unity Catalog table partitions for incoming requests

Correct answer

Serverless Model Serving autoscaling, including scale-to-zero

Objective/domain: Model Deployment

Source: Introduction to Databricks Model Serving

Question 3 A team needs to quickly generate baseline models and training notebooks for ML projects with minimal manual setup. Which Databricks capability should they leverage?

Answer choices

  1. A. Feature Store lineage in Unity Catalog
  2. B. AutoML
  3. C. A model alias in Unity Catalog
  4. D. Manual MLflow metric logging only

Correct answer

AutoML

Objective/domain: Databricks Machine Learning Foundations

Source: What is AutoML?

Question 4 A logged MLflow model artifact should become a governed Unity Catalog model. Which registration flow fits?

Answer choices

  1. A. Copy the pickle file to DBFS and treat the path as the registry
  2. B. Create a raw SQL binary string containing the model weights
  3. C. Skip MLflow because external artifacts cannot be registered
  4. D. Log the model, then register it with a three-level Unity Catalog name

Correct answer

Log the model, then register it with a three-level Unity Catalog name

Objective/domain: Model Registry and Governance

Source: Register a model in Unity Catalog using MLflow

Question 5 A team wants served model requests and responses logged for monitoring. Which Databricks feature captures them?

Answer choices

  1. A. Delta Lake history logs
  2. B. Inference Tables
  3. C. Spark UI timeline logs
  4. D. Unity Catalog schema comments

Correct answer

Inference Tables

Objective/domain: Model Deployment

Source: Inference tables for monitoring models

Question 6 For governed feature reuse, a feature computation requires Python logic registered within the Unity Catalog. What mechanism should be used to ensure this logic is executed on demand and governed by the catalog?

Answer choices

  1. A. An MLflow experiment run
  2. B. A dashboard widget
  3. C. A cluster-scoped temporary function only
  4. D. Unity Catalog registered UDFs for governed feature logic

Correct answer

Unity Catalog registered UDFs for governed feature logic

Objective/domain: Databricks Machine Learning Foundations

Source: Compute features on-demand using Python UDFs

Question 7 Before model training, a notebook user wants quick descriptive statistics for DataFrame columns before modeling. What should they call?

Answer choices

  1. A. df.show()
  2. B. DataFrame summary methods such as describe() or summary()
  3. C. df.groupBy().count() only
  4. D. df.randomSplit([0.8, 0.2])

Correct answer

DataFrame summary methods such as describe() or summary()

Objective/domain: Data Processing

Source: Databricks Certified Machine Learning Associate - Exam Guide

Question 8 A data scientist is training a model using scikit-learn and wants to log the trained model as an MLflow artifact. Which MLflow call should they use to accomplish this?

Answer choices

  1. A. mlflow.log_metric(key, value) for one metric at a time
  2. B. mlflow.log_param(key, value)
  3. C. mlflow.sklearn.log_model(sk_model=model, artifact_path="model")
  4. D. mlflow.set_tag("metrics", metrics_dict)

Correct answer

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

Objective/domain: MLflow and Experiment Tracking

Source: MLflow logging APIs

Question 9 A grid search is performed to optimize a model's hyperparameters. If the grid has three values for one hyperparameter and six values for another, what is the total number of model combinations evaluated?

Answer choices

  1. A. 3 models
  2. B. 18 models
  3. C. 6 models
  4. D. 9 models

Correct answer

18 models

Objective/domain: Model Development

Source: Model selection and tuning in Spark ML

Question 10 For exploratory data analysis, a nominal categorical field has no ordinal meaning and must become model-readable features. What encoding should be used?

Answer choices

  1. A. Ordinal encoding based on category order
  2. B. Log transformation
  3. C. Median imputation
  4. D. One-hot encoding

Correct answer

One-hot encoding

Objective/domain: Data Processing

Source: Databricks Certified Machine Learning Associate - Exam Guide

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