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Databricks Machine Learning Associate Exam Practice Questions, practice question pack
DATABRICKS · Machine Learning Associate

Databricks Machine Learning Associate Exam Practice Questions

99% pass rateUpdated 1 Nov 2026140 questions2 PDFs by email

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Every question with the correct answer and why the other options are wrong.

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Three of the 140 questions in this pack

Question 1

A data scientist is using MLflow to track their machine learning experiment. As a part of each of their MLflow runs, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values. All parent and child runs are being manually started with mlflow.start_run. Which of the following approaches can the data scientist use to accomplish this MLflow run organization?

  1. They can turn on Databricks Autologging
  2. They can specify nested=True when starting the child run for each unique combination of hyperparameter values
  3. They can start each child run inside the parent run's indented code block using mlflow.start_run()
  4. They can start each child run with the same experiment ID as the parent run
  5. They can specify nested=True when starting the parent run for the tuning process
Show answer and explanation

Correct answer: B. They can specify nested=True when starting the child run for each unique combination of hyperparameter values

MLflow supports hierarchical run organization through nested runs. When starting a child run, passing nested=True parameter creates a parent-child relationship between the currently active run and the new run being started. This allows a data scientist to have one parent run tracking the overall tuning process with multiple child runs, each representing a unique hyperparameter combination. The nested=True parameter must be specified on the child run calls, not the parent.

Why the other options are wrong

  • A. Databricks Autologging automatically logs metrics and parameters but does not create parent-child run hierarchies.
  • C. Simply nesting code blocks does not establish a parent-child relationship in MLflow without the nested=True parameter.
  • D. Using the same experiment ID does not create parent-child relationships; runs would be independent siblings in the same experiment.
  • E. The nested=True parameter on the parent run is incorrect syntax; it must be specified when starting child runs.

Question 2

Which of the following approaches can be used to view the notebook that was run to create an MLflow run?

  1. Open the MLmodel artifact in the MLflow run page
  2. Click the “Models” link in the row corresponding to the run in the MLflow experiment page
  3. Click the “Source” link in the row corresponding to the run in the MLflow experiment page
  4. Click the “Start Time” link in the row corresponding to the run in the MLflow experiment page
Show answer and explanation

Correct answer: C. Click the “Source” link in the row corresponding to the run in the MLflow experiment page

MLflow automatically tracks the source notebook that created a run when executed in Databricks. The MLflow experiment page displays a clickable 'Source' link in each run's row that directs to the notebook that was executed to generate that run. This provides direct access to view the notebook code and understand the experiment's origin.

Why the other options are wrong

  • A. The MLmodel artifact contains model serialization information, not notebook source code.
  • B. The 'Models' link navigates to registered models or model artifacts, not the source notebook.
  • D. The 'Start Time' link displays timestamp information for the run, not notebook source code.

Question 3

A data scientist is developing a machine learning pipeline using AutoML on Databricks Machine Learning. Which of the following steps will the data scientist need to perform outside of their AutoML experiment?

  1. Model tuning
  2. Model evaluation
  3. Model deployment
  4. Exploratory data analysis
Show answer and explanation

Correct answer: C. Model deployment

Databricks AutoML handles hyperparameter tuning, model selection, and evaluation automatically within the AutoML experiment process. It trains multiple models and identifies the best performer with detailed metrics. However, model deployment, moving the trained model into production and setting up serving infrastructure, occurs outside the AutoML experiment and requires separate steps for model registry, endpoint creation, and configuration.

Why the other options are wrong

  • A. Model tuning is a core AutoML function; the system performs extensive hyperparameter search and optimization.
  • B. Model evaluation is performed by AutoML; it automatically evaluates candidate models and ranks them.
  • D. Exploratory data analysis can be performed within AutoML experiments; the system handles data analysis as part of the process.

See all 31 free questions Get the full pack, US$39

How much of the Databricks Machine Learning Associate bank is free?

140 questions in the bank

Each step includes the one before, so buying only adds new questions.

  • You are here10questionsFree sampleThree on this page, all ten as a PDF.Download the sample
  • 3122% of the bankPractice pageFree on the site, every answer explained.Go to the practice page
  • 68the first half of the packHalf PackFree by email, as one PDF, every answer explained.Get the Half Pack
  • 140100%, the whole bankFull pack, US$39Plus the questions-only PDF, monthly updates, refund if you fail.Get the full pack
ABOUT THIS PACK

What’s in the Databricks Machine Learning Associate pack

  • 140 questions mapped to the Machine Learning Associate exam guide
  • Answers and explanations for every question, including the wrong options
  • A questions-only PDF for timed practice runs
  • Delivered by email shortly after checkout
  • Free monthly updates for as long as the exam is live
  • Pass or your money back

A Machine Learning Associate attempt costs US$200. This pack is US$39, paid once.

What makes the Machine Learning Associate exam hard

Machine Learning Associate is less a machine learning exam than a Databricks-tooling exam with machine learning in it. The current guide, dated March 2025, puts 38% of the marks on the Databricks Machine Learning platform itself, so knowing scikit-learn is table stakes and knowing how AutoML, Feature Engineering in Unity Catalog and MLflow behave on Databricks is what passes.

The exam is 48 scored questions in 90 minutes, with all machine learning code in Python (other data manipulation code may be SQL), and Databricks does not publish the pass mark.

Databricks Machine Learning is 38%: the ML Runtime, AutoML, the Feature Store and Feature Engineering in Unity Catalog, MLflow tracking and the model registry, and MLOps practices on the platform. Model Development at 31% covers preprocessing, Spark ML pipelines, scikit-learn and pandas on Databricks, and hyperparameter tuning. ML Workflows at 19% covers the lifecycle from exploratory data analysis to a registered model, and Model Deployment at 12% covers batch, streaming and real-time serving. Several distractors use the pandas method when the question wants the Spark one, so reading the API in the code sample matters.

About the exam

The Databricks Certified Machine Learning Associate validates entry-level machine learning on the Databricks Data Intelligence Platform: the Databricks ML platform, ML workflows, model development with Spark ML and scikit-learn, and model deployment. There are no prerequisites; Databricks recommends six months of hands-on ML experience on Databricks. The exam guide is dated 1 March 2025.

Questions before you buy

How quickly do I receive it?

Your PDF is prepared and sent to your email address after checkout, and you get a confirmation as soon as it is on its way.

Are updates included?

Yes. The pack is updated every month for as long as the exam is live, and updates are free for everyone who has bought it.

What if I fail the exam?

We refund the pack, or swap it for any other pack if you would rather keep studying. Sit the exam 7 to 30 days after buying, then send your official score report within 7 days of the exam date, as set out in the refund policy.

What is your pass rate?

99% of customers who prepare with a CertStash pack pass. If you do not, we refund the pack, or swap it for another pack.

Can I share it with colleagues?

Each purchase is licensed to one person. For a team, school or training organisation, email support@certstash.com for a licence that fits.

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