Databricks Certified Machine Learning Associate

An individual's capacity to utilise Databricks to carry out fundamental machine learning activities is evaluated by the Databricks Certified Machine Learning Associate certification test. This involves being able to comprehend and utilise Databricks Machine Learning and its features, such as AutoML, Feature Store, and specific MLflow features. Additionally, it evaluates the capacity to use Spark ML to construct machine learning workflows and make informed decisions inside those workflows. Finally, a person's understanding of sophisticated scaling properties of machine learning models is evaluated. It is anticipated of those who pass this certification test to be able to use Databricks and its companion tools to carry out fundamental machine learning activities.
Databricks Certified Machine Learning Associate The Databricks Certified Machine Learning Associate certification examination assesses an individual’s capacity to apply Databricks to carry out primary gadget gaining knowledge of responsibilities. This consists of an capacity to recognize and use Databricks Machine Learning and its abilties like AutoML, Feature Store, and pick out abilties of MLflow. It additionally assesses the capacity to make accurate choices in gadget gaining knowledge of workflows and put in force the ones workflows the usage of Spark ML. Finally, an capacity to recognize superior traits of scaling gadget gaining knowledge of fashions is assessed. Individuals who byskip this certification examination may be anticipated to finish primary gadget gaining knowledge of responsibilities the usage of Databricks and its related tools.

The minimally qualified candidate should be able to:

  • Use Databricks Machine Learning and its capabilities within machine learning workflows, including:
    • Databricks Machine Learning (clusters, Repos, Jobs)
    • Databricks Runtime for Machine Learning (basics, libraries)
    • AutoML (classification, regression, forecasting)
    • Feature Store (basics)
    • MLflow (Tracking, Models, Model Registry)
  • Implement correct decisions in machine learning workflows, including:
    • Exploratory data analysis (summary statistics, outlier removal)
    • Feature engineering (missing value imputation, one-hot-encoding)
    • Tuning (hyperparameter basics, hyperparameter parallelization)
    • Evaluation and selection (cross-validation, evaluation metrics)
  • Implement machine learning solutions at scale using Spark ML and other tools, including:
    • Distributed ML Concepts
    • Spark ML Modeling APIs (data splitting, training, evaluation, estimators vs. transformers, pipelines)
    • Hyperopt
    • Pandas API on Spark
    • Pandas UDFs and Pandas Function APIs
  • Understand advanced scaling characteristics of classical machine learning models, including:
    • Distributed Linear Regression
    • Distributed Decision Trees
    • Ensembling Methods (bagging, boosting)


Each attempt of the certification exam will cost the tester $200. Testers might be subjected to tax payments depending on their location. Testers are able to retake the exam as many times as they would like, but they will need to pay $200 for each attempt.

Test Aids

There are no test aids available during this exam.

Programming Language

All machine learning code within this exam will be in Python. In the case of workflows or code not specific to machine learning tasks, data manipulation code could be provided in SQL.


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