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Databricks ML Associate Certification: Exam Prep
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Databricks ML Associate Certification: Exam Prep

Exam-focused preparation for the Databricks ML Associate exam: AutoML, Feature Store, MLflow, Spark ML & Model Serving
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Created byAseem Mankotia
Last updated 8/2026
English

What you'll learn

  • Select the correct Databricks Runtime and cluster configuration for a given ML workload
  • Generate and interpret AutoML baseline models and their exported notebooks
  • Create and consume feature tables using Feature Engineering in Unity Catalog
  • Track experiments, compare runs, and register models with MLflow and Unity Catalog
  • Build reproducible Spark ML pipelines while avoiding data leakage in train/test splits
  • Train and choose between single-node scikit-learn and distributed Spark ML approaches
  • Tune hyperparameters with Hyperopt and SparkTrials versus grid/random search
  • Select appropriate evaluation metrics for classification and regression problems

Course content

12 sections • 12 lectures

Requirements

  • Working knowledge of Python
  • Basic understanding of machine learning concepts (classification, regression)
  • Familiarity with SQL and tabular data
  • Some exposure to Apache Spark or Databricks notebooks is helpful but not required
  • Databricks recommends roughly six months of hands-on platform experience (not mandatory)

Description

This course contains the use of artificial intelligence.

This course was produced with the assistance of artificial intelligence. AI tools were used to help draft and structure the lessons, generate the on-screen slides, and synthesize the voice narration. All content has been reviewed and curated by the instructor for accuracy and alignment with the current Databricks Certified Machine Learning Associate exam guide.

Prove you can build and ship machine learning on Databricks. This course delivers exam-focused preparation for the Databricks Certified Machine Learning Associate exam: a practical, platform-centric tour of the Databricks ML stack, from AutoML and the Feature Store to MLflow, Spark ML, and Model Serving. It is scenario-driven, so you learn to make the decisions the exam actually tests, not memorize theory.

What this course covers, mapped to Databricks' official exam-guide domains:

  • Databricks Machine Learning (38%): Databricks Runtime for ML and clusters, AutoML and its generated notebooks, Feature Engineering in Unity Catalog, MLflow Tracking, and the Model Registry / models in Unity Catalog
  • ML Workflows (19%): exploratory data analysis, data cleaning, feature engineering, and train/validation/test splitting that avoids data leakage, at scale with Spark and the pandas API on Spark
  • Model Development (31%): training scikit-learn and Spark ML pipelines, single-node vs distributed training, Hyperopt and SparkTrials tuning, cross-validation, and evaluation metrics for classification and regression
  • Model Deployment (12%): batch, streaming, and real-time inference with Model Serving, loading models from the registry, and scaling batch scoring with Spark UDFs

How this course prepares you: every topic shows the real Databricks and MLflow workflow and the Python or Spark code as it appears, then frames it the way the exam asks. You will practice the commonly confused pairs the exam loves: AutoML vs manual model building, Feature Engineering in Unity Catalog vs ad-hoc features, MLflow Tracking vs the Model Registry, Hyperopt SparkTrials vs distributed Spark ML training, single-node scikit-learn vs Spark ML, and batch vs streaming vs real-time Model Serving. Two full practice tests and a final exam-simulation chapter build your 90-minute, roughly 45-question timing strategy.

This is a read-and-understand, notebook-centric course. The free Databricks Community Edition or a trial workspace is enough to follow along, and no paid workspace is required. Ideal for data scientists, ML and data engineers, and early-career practitioners who want a recognized Databricks credential.

The exam is approximately 45 questions in 90 minutes, online and remotely proctored, closed-book, with a registration fee of about USD 200 plus tax, and the certification is valid for two years. Databricks updates the exam guide periodically, so always confirm the current domains and weights on the official Databricks Machine Learning Associate exam page before scheduling.

Enroll today and turn your Databricks ML skills into a credential employers recognize.

Who this course is for:

  • Data scientists and analysts expanding into machine learning on Databricks
  • Machine-learning and data engineers validating hands-on Databricks ML skills
  • Early-career professionals building a foundation in applied ML and MLOps
  • Anyone preparing for the Databricks Certified Machine Learning Associate exam

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