Databricks Machine Learning Professional: Practice Exam 2026

Prepare for Databricks Certified Machine Learning Professional exam with comprehensive full practice tests | CertShield

**Updated Nov/2025 | New Practice Test 3  | New Exam Outline

**Quality Check Done | Nov 2025

**Updated October 2025 | Additional Questions added for New Exam Outline

**Updated 31-March-2025

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Preparing for the Databricks Certified Professional Machine Learning Engineer exam?
This course provides 2026-aligned practice tests designed to match real exam complexity and Databricks’ latest machine learning ecosystem.

These practice exams help you master the complete Databricks ML lifecycle:

• Data preparation using Spark, Delta, and pandas API on Spark
• Feature engineering & feature management with Feature Store
• Experimentation, model development & tracking with MLflow
• AutoML workflows for tuning and baseline models
• Model serving on Databricks (batch & online)
• Unity Catalog security, lineage, permissions, and governance
• Deployment patterns, CI/CD, and production-ready ML pipelines
• Streaming ML with Structured Streaming
• Responsible AI, model monitoring, and model quality validation

Every question includes detailed explanations to help you understand why an answer is right and how Databricks applies ML engineering concepts in real-world scenarios.

This course prepares you to pass the Databricks ML Professional certification confidently — and strengthen your ability to build, deploy, and operate ML systems at scale.

WHO THIS COURSE IS FOR

• Data Scientists preparing for the Databricks ML Professional exam
• ML Engineers building ML pipelines on Databricks
• Data Engineers expanding into production ML systems
• AI Specialists deploying and monitoring ML models at scale
• Professionals seeking Databricks-recognized ML credentials

COURSE INCLUDES

• Full-length 2026 practice tests
• Realistic Databricks exam-style scenario questions
• Detailed explanations for every answer
• Coverage of all ML lifecycle tasks
• Lifetime access with continuous updates
• Domain-level readiness checks

Alignment with Real Exam: Questions mirror the content, difficulty level, and format of the actual exam you're preparing for. This lets you gauge your knowledge accurately and get familiar with the exam structure.

Comprehensive Coverage: The practice exam cover all major topics and concepts likely to appear on the real test.

Varying Difficulty Levels: Include questions with a mix of difficulty levels (easy, medium, hard) to simulate the real exam experience and identify your strengths and weaknesses.

Detailed Explanations: Every answer, correct or incorrect with clear, concise explanations. These help you understand why an answer is right/wrong, reinforcing your knowledge beyond simple memorization.

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Details of Databricks Certified Machine Learning Professional certification.

Purpose

This certification validates your ability to use Databricks Machine Learning features and capabilities to execute advanced production-level machine learning projects. It emphasizes end-to-end skills, from experimentation and model tracking to deployment and monitoring.

Target Audience

The Databricks Certified Machine Learning Professional certification is designed for experienced machine learning practitioners and data engineers who are ready to demonstrate their ability to build, deploy, and manage enterprise-scale ML systems on the Databricks Lakehouse Platform.

This certification is ideal for professionals who:

• Machine Learning Engineers — who design, train, and deploy ML models at scale using SparkML, MLflow, and the Databricks Feature Store.

• Data Scientists — who want to move beyond experimentation and demonstrate expertise in scalable training, distributed hyperparameter tuning (Optuna, Ray), and automated retraining workflows.

• MLOps Engineers — who specialize in CI/CD, environment management with Databricks Asset Bundles (DABs), and continuous monitoring using Lakehouse Monitoring for drift detection.

• Data Engineers and Analytics Engineers — who collaborate on feature pipelines and real-time inference systems across batch and streaming data environments.

• Technical Leads and Architects — who oversee ML lifecycle automation, model governance, and performance optimization in enterprise-scale Databricks deployments.

• AI Platform Specialists — who aim to validate their ability to integrate ML models into production with reliable deployment, monitoring, and scaling strategies.

Why This Certification Matters

• Confirms your ability to operationalize ML at scale using advanced Databricks capabilities.

• Recognizes expertise in end-to-end ML system design—from experimentation and tuning to serving and drift monitoring.

• Validates readiness to manage production-grade ML pipelines across multi-environment, multi-team Databricks ecosystems.

• Distinguishes you among professionals with real-world proficiency in Databricks MLOps, Feature Store, and Model Serving.

Exam Outline Blueprint

This exam covers:

1. Model Development - 44%

2. ML Ops - 44%

3. Model Deployment - 12%

Section 1: Model Development

Using Spark ML

• Identify when SparkML is recommended based on data, model, and use case.

• Construct an ML pipeline using SparkML.

• Apply the appropriate estimator and/or transformer given a use case.

• Tune and evaluate SparkML models using MLlib.

• Score SparkML models for batch or streaming use cases.

• Select SparkML or single-node models based on inference type (batch, real-time, streaming).

Scaling and Tuning

• Scale distributed training pipelines using SparkML and pandas Function APIs/UDFs.

• Perform distributed hyperparameter tuning using Optuna and integrate it with MLflow.

• Perform distributed hyperparameter tuning using Ray.

• Evaluate trade-offs between vertical and horizontal scaling in Databricks.

• Evaluate and select parallelization strategies (model vs. data parallelism).

• Compare Ray and Spark for distributing ML workloads.

• Use Pandas Function API to parallelize group-specific model training and inference.

Advanced MLflow Usage

• Utilize nested MLflow runs for tracking complex experiments.

• Log custom metrics, parameters, and artifacts programmatically.

• Create custom model objects with real-time feature engineering.

Advanced Feature Store Concepts

• Ensure point-in-time correctness to prevent data leakage.

• Build automated feature pipelines using the FeatureEngineering Client.

• Configure online tables for low-latency use cases via Databricks SDK.

• Design scalable streaming feature ingestion and computation pipelines.

• Develop on-demand features using feature serving for consistency across environments.

Section 2: MLOps

Model Lifecycle Management

• Describe and implement Databricks model lifecycle architectures.

• Map Databricks features to model lifecycle stages (e.g., dev, test, prod).

Validation Testing

• Implement unit tests for notebook functions.

• Identify testing types (unit, integration) in different environments.

• Design integration tests that include feature engineering, training, evaluation, deployment, and inference.

• Compare approaches to organizing functions and tests.

Environment Architectures

• Design scalable Databricks ML environments using best practices.

• Define and configure Databricks ML assets via Databricks Asset Bundles (DABs).

Automated Retraining

• Implement automated retraining triggered by data drift or performance degradation.

• Develop a strategy for selecting top-performing models during retraining.

Drift Detection and Lakehouse Monitoring

• Apply statistical tests from Lakehouse Monitoring drift metrics.

• Identify appropriate data table types for monitoring use cases.

• Build monitors for snapshot, time-series, or inference tables.

• Design alerting for drift metrics exceeding thresholds.

• Detect data drift using baselines or time window comparisons.

• Evaluate model performance trends using inference tables.

• Define custom metrics and evaluate based on granularity and feature slicing.

• Monitor endpoint health (latency, request rate, errors, CPU/memory usage).

Section 3: Model Deployment

Deployment Strategies

• Compare deployment strategies (blue-green, canary) for high-traffic applications.

• Implement rollout strategies using Databricks Model Serving.

Custom Model Serving

• Register custom PyFunc models and log artifacts in Unity Catalog.

• Query custom models using REST API or MLflow Deployments SDK.

• Deploy custom model objects through MLflow SDK, REST API, or UI.

Prerequisites

• No Formal Prerequisites: However, 1+ years of experience in machine learning tasks is highly recommended.

• Working knowledge of Python and major libraries that support machine learning like scikit-learn, SparkML, and MLflow

• Working knowledge of Lakehouse Monitoring and Databricks Model Serving

• Familiarity with the major topics in machine learning in Databricks documentation

Preparation Resources

• Official Exam Guide

• Databricks Academy Courses: Explore relevant courses offered by Databricks.

• Practice Exams like CertShield from Udemy

• Hands-on Practice: The more you work with Databricks ML features, the better!

Exam Details

• Number of items: 59 scored multiple-choice questions

• Time limit: 120 minutes

• Registration fee: USD 200, plus applicable taxes as required per local law

• Delivery method: Online Proctored

• Test aides: none allowed

• Prerequisite: None required; course attendance and 1 year of hands-on experience in Databricks is highly recommended

• Validity: 2 years

• Recertification: Recertification is required every two years to maintain your certifi ed

Reasons to Consider This Certification

• Databricks-Specific: Demonstrates expertise with the popular Databricks platform.

• Practical Focus: Emphasizes productionizing ML models, not just theoretical concepts.

• Career Growth: Can open opportunities in Databricks-centric organizations