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Skills measured

• Implement responsible machine learning (5–10%)
• Manage Azure resources for machine learning (25–30%)
• Run experiments and train models (20–25%)
• Deploy and operationalize machine learning solutions (35–40%)

How to study the DP-100 Exam

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

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Target Audience & Requirements

The candidates for this Microsoft exam are Azure Data Scientists. These professionals have expertise in applying their knowledge of machine learning and data science to run and implement ML workloads on Azure. This is particularly in the usage of Azure ML Service. These applicants are the experts in planning and creating the appropriate working environments for data science workloads within Azure. They also train predictive models and run data experiments. The individuals who want to earn ACE college credit can also take this certification test.

The Microsoft DP-100: Designing & Implementing a Data Science Solution on Azure test has no official requirement. However, the candidates must develop an in-depth understanding of the exam topics. They should also have expertise in model optimization and management and ML models deployment within the production.

Skills Covered

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

• 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.

• 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.

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Microsoft DP-100 Korean Exam Syllabus Topics: