Databricks · DB-MLA · Associate

Databricks Machine Learning Associate — Practice Questions and Mock Exam

Name: Pass-IT
Author: Pass-IT

Prepare for DB-MLA with original practice questions and clear answer explanations. Ask Alex, your AI tutor, when you need more detail, use your results to identify topics to review, and practice your pacing with timed mock exams.

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48Mock exam questions

90minTime limit

Checked against Databricks · August 2026 · Current exam version

Overview

About the exam

The Databricks Certified Machine Learning Associate exam validates the ability to build, train, and deploy machine learning models on the Databricks platform. It covers Databricks-specific ML tooling including MLflow for experiment tracking and the model registry, Feature Store for feature management, and AutoML for rapid prototyping, alongside data processing, model development, and model deployment. Candidates must demonstrate proficiency in operationalizing ML models within the Databricks Machine Learning workspace.

This certification is designed for data scientists and ML engineers with six or more months of experience using Databricks for machine learning tasks. It demonstrates foundational proficiency in the Databricks ML lifecycle from data processing through deployment.

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Try five DB-MLA questions

Try five practice questions from the app’s current Databricks Certified Machine Learning Associate question bank, with answers and explanations.

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

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Question pool

362 practice questions

Use the Pass-IT question pool to practice for DB-MLA. Mock exams are set to 48 questions in 90 minutes.

Pool details: DB-MLA

Objectives in the guide48 objectives listed in the official guide

across 4 domains in the official exam guide

Pool size362 questions

= The pool size is equivalent to 7 sets of 48 questions; this does not mean that each mock exam uses a separate set.

Recorded as checked against sources362 of 362

questions recorded as having their answer, options, and explanation checked against official Databricks documentation

How questions are made →

Exam domains

What's on the exam

Databricks Machine Learning is the heaviest domain at 38%, covering MLflow experiment tracking, the Unity Catalog model registry, Feature Store tables, and AutoML — Databricks-specific tooling rather than general ML theory. Model development follows at 31%, testing algorithm selection, hyperparameter tuning with Hyperopt or cross-validation, and the classification and regression metrics that apply regardless of platform.

Data processing (19%) and model deployment (12%) round out the exam: feature engineering on Spark DataFrames, including imputation, encoding, and outlier handling, and comparing batch, streaming, and real-time serving approaches for a given endpoint. Together, Databricks Machine Learning and Model Development make up 69% of the exam, so platform fluency matters as much as general ML knowledge.

Exam blueprint: DB-MLA

Databricks Machine Learning38%

Apply MLOps practices with ML runtimes and AutoML to speed up model and feature selection, build feature-store tables in Unity Catalog for training and scoring, and use the MLflow Client API to track runs and promote models through the registry.

≈ 30 h

Data Processing14%

Compute summary statistics and remove outliers on Spark DataFrames, create visualizations to compare categorical and continuous features, and apply imputation, one-hot encoding, and log scale transformations to prepare data for modeling.

≈ 11 h

Model Development35%

Select appropriate algorithms and mitigate data imbalance when developing a training pipeline, and tune hyperparameters using methods such as Hyperopt, grid search, and cross-validation. Also covers evaluating models with common classification and regression metrics and assessing the bias-variance tradeoff.

≈ 28 h

Model Deployment13%

Compare batch, real-time, and streaming model serving approaches, deploy custom models to serving endpoints for real-time and batch inference, and split traffic between endpoints for real-time inference.

≈ 10 h

The shares below describe how questions in the Pass-IT practice pool are distributed across domains. Refer to Databricks for the official exam blueprint.

Format

Exam format and question types

The exam consists of 48 scored questions, a mix of multiple-choice and multiple-select formats, inside a 90-minute window; unscored pilot items may also appear. Expect Databricks-specific scenarios: logging a run with the MLflow Client API, registering a model to the Unity Catalog registry, or distinguishing an online feature table from an offline one for a given use case.

Question types: DB-MLA

Multiple Choice100%

Select the single answer that best meets the question’s requirements.

See Databricks for official question-format information. The shares shown describe the Pass-IT practice pool; they do not establish the proportions on the official exam.

Planning

Preparing for DB-MLA

The exam is delivered online with a remote proctor or at a test center, offered in English, Japanese, Portuguese (BR), and Korean. The credential holds for two years, and recertifying means passing the current version of the exam.

Preparation and logistics: DB-MLA

Preparation

Illustrative study time50–120 h

illustrative planning range: 50 h with relevant experience to 120 h when starting out; your needs may fall outside this range

LevelAssociate

Recommended backgroundNone required. 6+ months hands-on Databricks ML experience recommended.

Taking and maintaining the certification

DeliveryOnline proctored or test center

Retake policyNo mandatory waiting period. Retake fee applies.

Certification validity2 years

Recertification required every 2 years by taking the current exam version.

Official guideView on provider site

Study notes

Common pitfalls

Topics to review: DB-MLA

01Code vs Model Promotion
Defaulting to promoting a trained model across environments when the scenario calls for promoting and retraining from code, or vice versa, leads to wrong answers on MLOps-strategy questions.
02Estimators vs Transformers
Confusing an estimator, which learns from data via fit, with a transformer, which applies a fixed transformation, in a Spark ML pipeline leads to wrong answers on pipeline-construction questions.
03Cross-Validation Trade-offs
Underestimating how many models a grid search combined with cross-validation actually trains, and when a plain train/validation split is the better trade-off, leads to wrong answers on tuning questions.
04Endpoint Traffic Split
Not knowing how to split traffic between model-serving endpoints for a staged or A/B rollout leads to wrong answers on realtime-inference deployment questions.
05Imbalance Mitigation
Reaching for accuracy alone instead of an imbalance-aware technique or metric when training data is skewed leads to wrong answers on model-development questions about class imbalance.

Practice with questions and explanations in Pass-IT →

Questions

Frequently asked questions

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.

Which pitfalls should I review when preparing for Databricks Certified Machine Learning Associate?

Topics to review include Code vs Model Promotion, Estimators vs Transformers, Cross-Validation Trade-offs, Endpoint Traffic Split, Imbalance Mitigation. Work through examples to check that you understand the distinctions and can explain your answer.

How is the Machine Learning Associate exam weighted?

Databricks Machine Learning is the dominant area at 38%, with model development at 31% and data processing at 19%. Model deployment closes it at 12%. Seven questions in ten are therefore about the platform tooling and about building models inside it.

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.

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.

What comes after the Machine Learning Associate?

The Machine Learning Professional exam is the step up, and its shape is different: model development and MLOps carry 44% each, so operations become half the exam. The Generative AI Engineer Associate is the sideways move if your work is shifting to foundation models. Neither requires the associate certification first.

One certification, 12 months

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