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

• 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.
• 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.

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Languages: English, Japanese, Chinese (Simplified), Korean

Retirement date: none

This exam measures your ability to accomplish the following technical tasks: manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions; and implement responsible machine learning.

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

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