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To nail DP-100, you will need to scrutinize the below-mentioned areas:

• Deploy and Consume Models

  The last segment is all about deployment and consumption models. Topics like evaluating compute options, creating production compute targets, batch inferencing pipeline creation, and running this pipeline efficiently are well covered within such a scope.

• Set up Azure ML Workspace

  The first domain gives considerable attention to skills related to the Azure ML workspace. So, the test-takers have a chance to learn about workspace settings, the management of workspace using Azure ML, and registering in addition to maintaining the datastores.

• Manage and Optimize Models

  Using automated ML for the optimal model creation, hyperdrive to tune hyperparameters, model management, and knowing the crucial model explainers to interpret models are some of the key topics explained in this portion.

• Execute Experiments & Train Models

  This objective imparts updated understanding about the concepts like creating models by using Azure ML Designer, custom code modules in Designer, defining a pipeline data flow, and an experiment running by using Azure Machine Learning SDK.

The Microsoft DP-100 test helps candidates check their machine learning knowledge and skills and prove themselves as qualified data scientists.

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

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