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3. Models Management and Optimization (20-25%):

• Usage of hyperdrive for the tuning of hyperparameters: This domain will evaluate the ability of the applicants to define search space, primary metrics, and early termination alternatives. It also expects their skills in sampling techniques selection and model discovery that require optimal hyper-parameter values.
• Usage of model explainers for the interpretation of models: The learners have to demonstrate their competence in choosing model interpreters and generating the features of important data.
• Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.
• Usage of Automated Machine Learning for the creation of optimal models: This section requires your skills in retrieving the best models and getting data for Automated ML runs. It also covers competence in defining primary metrics, selecting pre-processing alternatives, and determining the algorithms to be searched. The candidates should be able to use the Automated Machine Learning from Azure ML SDK as well as Automated ML interface within Azure ML studios.

Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx

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Microsoft DP-100 certification exam is a valuable credential for data scientists and machine learning engineers who want to demonstrate their proficiency in designing and implementing data science solutions on Azure. DP-100 exam covers a wide range of topics related to data science and machine learning and requires candidates to have a deep understanding of Azure data services. To prepare for the exam, candidates can take advantage of various resources provided by Microsoft, including online training courses, study guides, and practice exams.