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Databricks Certified Machine Learning Professional (Machine-Learning-Professional)

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Vendor

Databricks

Certification

ML Engineer

Content

165 Qs

Status

Verified

Updated

1 day ago

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Exam Overview

The Databricks Certified Machine Learning Professional certification signifies a candidate's advanced proficiency in building, deploying, and managing machine learning solutions on the Databricks Lakehouse Platform. This credential validates expertise in leveraging Databricks' robust capabilities, including MLflow for comprehensive MLOps, distributed machine learning frameworks, and the Databricks Feature Store for streamlined feature engineering. Earning this certification demonstrates an individual's ability to move beyond experimental models to production-ready, scalable, and reliable ML systems. It is invaluable for professionals aiming to lead complex ML projects, optimize model performance in production environments, and drive significant business value by transforming data into actionable intelligence at scale within the Databricks ecosystem.

Questions

60

Passing Score

700/1000

Duration

120 Minutes

Difficulty

Expert

Level

Professional

Skills Measured

MLflow for MLOps: Expertise in using MLflow for experiment tracking, model packaging, model registry management, and MLOps automation, including lifecycle management from development to production.
Distributed Machine Learning: Proficiency in implementing and scaling machine learning models using distributed computing frameworks on Databricks, including Spark MLlib, Horovod, and understanding the use of Petastorm for data loading.
Feature Engineering and Management: Deep understanding and practical application of feature engineering techniques, including leveraging the Databricks Feature Store for creating, sharing, and managing reusable features across models.
Model Deployment and Monitoring: Ability to deploy machine learning models for batch and real-time inference, configure model serving endpoints, and implement strategies for continuous model monitoring, performance evaluation, and re-training.
Advanced ML Techniques and Best Practices: Knowledge of advanced topics such as hyperparameter tuning (e.g., Hyperopt), model interpretability (e.g., SHAP, LIME), responsible AI practices, and optimizing model performance and cost efficiency on Databricks.

Career Path

Target Roles

Machine Learning Engineer Data Scientist (with a focus on production ML) MLOps Engineer

Common Questions

Is the material up to date?

Yes. We update our question bank weekly to match the latest Databricks standards. You get free updates for 90 days.

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You get instant access to both the **PDF** (for reading) and our **Premium Test Engine** (for exam simulation).

Is there a guarantee?

Absolutely. If you fail the Machine-Learning-Professional exam using our materials, we offer a full money-back guarantee.

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