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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.

Obligatory Prerequisites

Officially, there is no prior work-experience or educational expertise related to DP-100 exam. Anyone, willing to make it big in the world of data science, can go for it. However, industry pundits say that beginner-level expertise in concepts like running data experiments and machine learning will make the exam journey a lot more simplified and easy to accomplish.

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The candidates who want to take the Microsoft DP-100 exam are expected to have competence in its objectives. This means that they need to understand the scope of topics covered in this certification test. They are as follows:

1. Azure ML Workspace Set-Up (30-35%):

• Azure ML workspace creation: This area focuses on the students’ skills in creating Azure ML workspace, operating workspaces by using Azure ML studio, and configuring workspace settings.
• Experiment compute contexts management: The test takers must be able to create compute instances and compute targets for training and experiments. They have to know how to establish suitable compute specifications for training workloads.
• Data objects management within Azure ML workspace: The applicants should have competence in the creation and management of datasets, as well as maintenance and registration of datastores.

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

4. Models Deployment and Consumption (20-25%):

• Designer pipeline publishing as a web service: The candidates should show their knowledge of target compute resources creation, inference pipelines configuration, and deployed endpoints consuming.
• Creation of pipelines for batch inferencing: This subject area covers your competence in running batch inferencing pipelines and obtaining outputs as well as publishing batch inferencing pipelines.
• Production computes targets creation: The test takers should perform their skills in compute options evaluation for deployment and consideration of security for deployed services.
• Model-as-a-Service deployment: This subtopic will measure the individuals’ expertise in configuring deployment settings, troubleshooting issues with deployment containers, and consuming deployed services.

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