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Databricks · DB-MLA · Associate

Databricks Machine Learning Associate — Practice Questions and Mock Exam

The Databricks Certified Machine Learning Associate exam validates the ability to build, train, and deploy machine learning models on the Databricks platform.

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48Questions per Pass-IT mock exam
90minPass-IT mock exam time limit

Checked against Databricks · October 2026 · Current exam version

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Databricks Machine Learning1 / 5

How does AutoML in Databricks handle the problem of choosing the right ML algorithm?

AlexFull explanation from Alex

AutoML workflow: 1) Specify: dataset, target column, problem type (classification/regression/forecasting), and evaluation metric. 2) AutoML explores: data preprocessing (encoding, imputation), feature selection, algorithm selection, hyperparameter search. 3) Results: ranked list of models with metrics, generated notebooks showing exact code for each approach. 4) Best practice: use AutoML as a starting point, then customize the top-performing notebook. The generated notebooks use standard libraries (scikit-learn, XGBoost, LightGBM) so they are fully portable and editable.

Sourcedocs.databricks.com

Model Deployment3 / 5

A machine learning engineer wants to roll back a model deployment on Databricks after discovering issues in production. What is the fastest approach?

AlexFull explanation from Alex

Model aliases are useful in deployment workflows because they decouple the human deployment label from an immutable model version. Batch inference can load models by alias directly, but Databricks documents Model Serving endpoint workflows as resolving the alias to a model version and updating the endpoint configuration to serve that version. A rollback therefore updates the endpoint served entity to the previous model version while preserving the endpoint URL.

Sourcedocs.databricks.com

Model Development5 / 5

What is the curse of dimensionality and how does it affect machine learning models?

AlexFull explanation from Alex

Practical implications: in high dimensions, the nearest neighbor of any point is almost as far as the farthest point, making KNN ineffective. Distance-based clustering deteriorates. Linear models may need exponentially more data. Mitigations include: feature selection (remove irrelevant features), dimensionality reduction (PCA, autoencoders), regularization (L1/L2 penalties), and using algorithms less sensitive to dimensionality (tree-based models handle high dimensions relatively well because they select informative features at each split).

Sourcescikit-learn.org

How to prepare for DB-MLA with Pass-IT.

Currently 362 practice questions for DB-MLA, with new ones added regularly. Focus on what you still need to learn.

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Conversation preview · DB-MLA

Comparing models with AutoML

  1. You

    How does AutoML help choose an algorithm?

  2. Alex · AI tutor

    It trains multiple candidates and compares their validation performance.

    Read the Databricks documentation ↗
  3. You

    Can I inspect the resulting model work?

  4. Alex · AI tutor

    Yes. It generates notebooks for model candidates so you can inspect and continue the work.

    Read the Databricks documentation ↗

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DB-MLA · Question excerpt

Comparing models with AutoML

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How does AutoML in Databricks handle the problem of choosing the right ML algorithm?

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It automatically trains multiple algorithms in parallel, comparing their performance on a validation set and generating notebooks for the best-performing models

AutoML compares candidate models on validation data and provides notebooks for further work.

Read the Databricks documentation ↗

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Your DB-MLA mock exam

Questions
48
Minutes
90

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Length and time limit of the Pass-IT mock exam.

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You need at least 80% Readiness and active, paid access to the certification when you take the exam. Take the exam within your access period and submit official proof of failing within 14 days after the exam.

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Question counts and mock exam settings from the current Pass-IT catalog.

Exam domains

What you practice for DB-MLA.

Exam topics, relevant practice and sources you can check.

Use Databricks ML toolsSee the guide for weighting138 QuestionsPass-IT questions

Manage features, experiments and models through their lifecycle.

  • Explain MLOps practices and the advantages of ML runtimes and AutoML for model and feature selection.
  • Create and populate Unity Catalog feature tables, train and score with their features, and compare account-level versus workspace stores and online versus offline tables.
  • Log metrics, artifacts and models in MLflow, inspect the UI and use the Client API to find the best run.
  • Register models in Unity Catalog with the MLflow Client API and explain the benefits over the workspace registry.
  • Choose whether to promote code or models, manage model tags and use aliases to promote a challenger to champion.
Official Databricks objectives
Explore and prepare training dataSee the guide for weighting52 QuestionsPass-IT questions

Understand feature distributions and choose suitable preprocessing.

  • Compute Spark DataFrame summaries, visualize categorical or continuous features and compare pairs using suitable methods.
  • Remove outliers using standard deviation or the interquartile range.
  • Compare mean, median and mode imputation and apply the appropriate replacement for missing values.
  • Apply one-hot encoding when suitable for the data and model, and recognize when a log transformation is appropriate.
Official Databricks objectives
Train and evaluate modelsSee the guide for weighting125 QuestionsPass-IT questions

Choose algorithms and validate their performance carefully.

  • Select an algorithm, address class imbalance and build a training pipeline with estimators and transformers.
  • Tune hyperparameters with Hyperopt fmin, random, grid or Bayesian search and parallelize tuning of single-node models.
  • Compare cross-validation with a train-validation split, run it during fitting and calculate how many models a grid search will train.
  • Choose classification or regression metrics such as F1, log loss, ROC/AUC, RMSE, MAE or R-squared for the objective, and exponentiate log-transformed values before interpreting predictions or calculating relevant metrics.
  • Assess how model complexity and the bias-variance tradeoff affect performance.
Official Databricks objectives
Deploy models for inferenceSee the guide for weighting47 QuestionsPass-IT questions

Choose how a trained model will make predictions.

  • Compare batch, real-time and streaming serving for the workload.
  • Deploy custom models to endpoints, query real-time predictions and split inference data between endpoints.
  • Perform batch inference with pandas and explain streaming inference with Delta Live Tables.
Official Databricks objectives
Question formats you can practice
Multiple Choice
Select one answer.

These are Pass-IT practice formats. Check the Databricks certification page for the official exam information.

Frequently asked questions

Your preparation

How much study time should I plan for DB-MLA?

For planning, Pass-IT estimates roughly 50–120 hours. Relevant experience may put you toward the lower end; starting from scratch may put you toward the higher end. Your needs may fall outside this range. This is not a time requirement from Databricks or a promise that you will be exam-ready within that time.

What experience does the Machine Learning Associate assume?

Databricks recommends six or more months of hands-on machine learning work on the platform, and requires no certification to book. The catalog budget is around 80 hours. Because the platform tooling carries the biggest weight, time in the workspace counts for more here than general modelling theory.

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What language are Pass-IT questions in?

Questions in the app are currently in English. Translations of the sample questions on this page apply only to this preview. Check with Databricks for the languages available for the official exam.

How long does my DB-MLA access last?

Access covers this certification for 12 months. You pay once, with no automatic renewal.

When does the money-back guarantee apply?

The guarantee refunds the Pass-IT purchase price for this certification. You need eligible paid access and a Readiness Score of at least 80% from your practice results when you take the exam. This score can only reach 80% after at least 50 answered questions and is not a validated probability of passing. Take the exam during your access period and submit official proof of failing within 14 days. Guarantee terms

About the DB-MLA exam

Where can I take the DB-MLA exam?

Online proctored or test center.

How long is the Databricks Certified Machine Learning Associate exam?

The Databricks Certified Machine Learning Associate exam has 48 questions and a 90-minute time limit.

Does the Machine Learning Associate certification expire?

Yes, two years after you pass. Recertification is by exam only, using whatever version is current at the time. There is no credit-based renewal route at Databricks.

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Money back if you fail with at least 80% Readiness, for eligible purchases under the guarantee terms.

Guarantee terms

The guarantee refunds the Pass-IT purchase price for this certification. You need eligible paid access and a Readiness Score of at least 80% from your practice results when you take the exam. This score can only reach 80% after at least 50 answered questions and is not a validated probability of passing. Take the exam during your access period and submit official proof of failing within 14 days.

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