Beginner

Exam Overview

Everything you need to know about the Databricks Machine Learning Professional certification exam before you start studying — format, scoring, domain breakdown, study plans, and the registration process.

Exam At a Glance

The Databricks Machine Learning Professional certification validates your ability to design, build, and manage production machine learning solutions using the Databricks Lakehouse Platform. It is one of the most respected data and ML certifications in the industry, demonstrating advanced proficiency with MLflow, Feature Store, model serving, and ML pipeline automation.

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Key facts: 60 questions • 120 minutes • ~70% to pass • $200 USD • Valid for 2 years • Proctored online via Kryterion

Question Format

The exam consists of multiple-choice questions:

  • Single-select — One correct answer out of four options (majority of questions)
  • Multi-select — Two or more correct answers (clearly marked as "select ALL that apply")

There is no penalty for guessing. Never leave a question blank. If you are unsure, eliminate what you can and make your best choice.

Scoring

Databricks uses a percentage-based scoring model. The minimum passing score is approximately 70%. You need to answer roughly 42 out of 60 questions correctly to pass. Your score report will indicate your overall percentage and performance by domain.

The Exam Domains

The exam covers four main areas. Understanding these domains is critical for prioritizing your study time.

Experimentation — ~30%

MLflow tracking, experiment management, AutoML, hyperparameter tuning with Hyperopt, distributed training, and model evaluation. This is the largest domain.

Model Lifecycle Management — ~30%

MLflow Model Registry, model versioning, stage transitions, model serving endpoints, batch inference, real-time predictions, and A/B testing.

Feature Store & Data Management — ~20%

Databricks Feature Store, feature table creation and management, online/offline feature serving, Spark ML pipelines, and data preparation for ML.

ML Pipelines & Production — ~20%

Delta Live Tables, Databricks Workflows, CI/CD for ML, pipeline orchestration, monitoring, alerting, and production best practices.

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Study priority: Experimentation and Model Lifecycle Management together account for ~60% of the exam. If you are short on time, prioritize these two domains along with hands-on MLflow practice.

Prerequisites

Databricks recommends the following before attempting this exam:

  • Passed the Databricks Machine Learning Associate certification (recommended but not required)
  • At least 6 months of hands-on experience with Databricks for ML workloads
  • Strong understanding of Python, PySpark, and pandas
  • Experience with MLflow (tracking, registry, serving)
  • Familiarity with Delta Lake and the Lakehouse architecture

4-Week Accelerated Study Plan

For experienced practitioners who want to move quickly:

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Week 1: Feature Store & data management + Spark ML pipelines + hands-on labs
Week 2: Experimentation domain + MLflow deep dive + AutoML + Hyperopt
Week 3: Model lifecycle + Model Registry + Model Serving + deployment patterns
Week 4: ML Pipelines & Workflows + practice exam + review weak areas

6-Week Comprehensive Study Plan

For those newer to Databricks ML or wanting thorough preparation:

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Week 1: Databricks Lakehouse fundamentals + Delta Lake refresher + workspace setup
Week 2: Feature Store & data management + Spark ML pipelines + feature engineering hands-on
Week 3: MLflow tracking + experiment management + AutoML exploration
Week 4: Hyperopt + distributed training + model evaluation metrics
Week 5: Model Registry + Model Serving + batch/real-time inference + deployment patterns
Week 6: Workflows + DLT + CI/CD + practice exam + review all weak areas

Registration Process

  1. Create an account on the Databricks Academy portal at academy.databricks.com
  2. Navigate to Certifications and select Machine Learning Professional
  3. You will be redirected to Kryterion/Webassessor for scheduling
  4. Choose an online proctored session and select your preferred date and time
  5. Pay the $200 USD exam fee
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Pro tip: Databricks offers free learning paths on Databricks Academy that align directly with the exam objectives. Complete the "Machine Learning Professional" learning path before scheduling your exam. It includes hands-on labs in a real Databricks environment.

Exam-Taking Strategy

Time Management

You have 120 minutes for 60 questions, giving you exactly 2 minutes per question. Use this strategy:

  • First pass (60 minutes): Answer every question you are confident about. Flag anything that takes more than 90 seconds.
  • Second pass (40 minutes): Return to flagged questions with fresh perspective. Eliminate wrong answers, then choose.
  • Final review (20 minutes): Review all answers, especially flagged questions. Check for misreads.

Key Exam Patterns

The Databricks ML Professional exam has distinct patterns you should recognize:

  • MLflow-centric: Many questions test MLflow APIs, tracking concepts, and Model Registry workflows
  • Code-based: Expect questions with code snippets asking you to identify errors, fill in missing parameters, or predict output
  • Scenario-based: Real-world ML engineering scenarios asking which Databricks feature or API to use
  • Best practices: Questions about recommended patterns for production ML on Databricks

What Is Next

Now that you understand the exam format and have a study plan, it is time to dive into the first domain. In the next lesson, we cover Feature Engineering — Databricks Feature Store, Spark ML pipelines, and data preparation for ML, with practice questions to test your knowledge.