Microsoft Microsoft Azure DP-100J

Conclusion

Data science is a lucrative career option and you can explore it without any hassle if you’ve managed to taste success in the Microsoft DP-100 exam, which is known to create a skilled workforce of Azure data scientists. However, a great outcome in such a test will only come if the aspirant is referring to the updated and recent study resources like the official courses provided by the exam vendor itself.

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

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Who should take the DP-100 exam

The Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam certification is an internationally-recognized validation that identifies persons who earn it as possessing skilled as a Microsoft Certified Azure Data Scientist Associate. If candidates want significant improvement in career growth needs enhanced knowledge, skills, and talents. The Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam certification provides proof of this advanced knowledge and skill. If a candidate has knowledge of associated technologies and skills that are required to pass Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam then he should take this exam.

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Skills Covered

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

• 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.

• 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.

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