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100+ Free Databricks ML Professional Practice Questions

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2026 Statistics

Key Facts: Databricks ML Professional Exam

59

Scored Questions

Databricks official exam page

120 min

Exam Duration

Databricks official exam page

$200

Registration Fee

Databricks certification pricing

~70%

Passing Score

Widely reported benchmark

2 years

Validity Period

Databricks recertification policy

3

Exam Domains

Model Development 44%, MLOps 44%, Deployment 12%

Databricks Certified Machine Learning Professional is a 120-minute proctored exam with 59 scored multiple-choice questions and a $200 registration fee. The exam covers Model Development (44%), MLOps (44%), and Model Deployment (12%). Key topics include SparkML pipelines, MLflow (tracking, registry, autologging), Feature Store, Databricks Asset Bundles for CI/CD, Lakehouse Monitoring for drift detection, and Model Serving endpoints. No prerequisites, but 1+ years of hands-on Databricks ML experience is recommended. Validity is 2 years.

About the Databricks ML Professional Exam

The Databricks ML Professional certification validates advanced skills in building scalable ML pipelines with SparkML, managing the ML lifecycle with MLflow, implementing MLOps practices with Databricks Asset Bundles, and deploying production-grade ML systems with comprehensive monitoring and drift detection.

Questions

59 scored questions

Time Limit

120 minutes

Passing Score

~70%

Exam Fee

$200 (Databricks)

Databricks ML Professional Exam Content Outline

44 questions (44%)

Model Development

SparkML pipelines, feature engineering, distributed training, hyperparameter tuning, evaluation, and AutoML

44 questions (44%)

MLOps

MLflow tracking and registry, Asset Bundles, CI/CD, testing, monitoring, drift detection, and governance

12 questions (12%)

Model Deployment

Model Serving endpoints, batch inference, streaming inference, pyfunc models, and rollout strategies

How to Pass the Databricks ML Professional Exam

What You Need to Know

  • Passing score: ~70%
  • Exam length: 59 questions
  • Time limit: 120 minutes
  • Exam fee: $200

Keys to Passing

  • Complete 500+ practice questions
  • Score 80%+ consistently before scheduling
  • Focus on highest-weighted sections
  • Use our AI tutor for tough concepts

Databricks ML Professional Study Tips from Top Performers

1Build a complete end-to-end ML project on Databricks: data prep, feature store, training, MLflow tracking, serving, and monitoring
2Master MLflow deeply — autologging, model signatures, pyfunc custom models, and Model Registry with Unity Catalog
3Practice SparkML Pipeline construction with CrossValidator, ParamGridBuilder, and multiple evaluation metrics
4Understand Databricks Asset Bundles (DABs) for deploying ML workflows as code through CI/CD
5Study Lakehouse Monitoring concepts: data drift, prediction drift, KS test, and automated alerting

Frequently Asked Questions

How many questions are on the Databricks ML Professional exam?

The exam has 59 scored multiple-choice questions to be completed in 120 minutes. Additional unscored items may be included for statistical purposes.

What is the passing score for Databricks ML Professional?

Databricks does not publish an exact passing score, but it is widely reported as approximately 70%. Always check the latest exam guide on databricks.com for current details.

How much does the Databricks ML Professional exam cost?

The exam registration fee is $200 USD per attempt. The registration is valid for 12 months from purchase.

What are the exam domains and their weightings?

As of the latest exam guide, the three domains are Model Development (44%), MLOps (44%), and Model Deployment (12%). MLflow, SparkML, Feature Store, and Lakehouse Monitoring are core topics.

Is there a prerequisite for the ML Professional exam?

There are no strict prerequisites, but Databricks recommends 1+ years of hands-on experience performing ML tasks on the Databricks platform. The ML Associate certification is a useful stepping stone.

How should I prepare for Databricks ML Professional in 2026?

Focus on hands-on practice with MLflow (tracking, registry, autologging), SparkML pipelines, Feature Store, Databricks Asset Bundles, and Lakehouse Monitoring. Build an end-to-end ML project on Databricks covering data prep through deployment and monitoring.