Microsoft Designing and Implementing a Data Science Solution on Azure Exam Certification Details:

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

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

• 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.
• Production computes targets creation: The test takers should perform their skills in compute options evaluation for deployment and consideration of security for deployed services.

2. Train Models & Run Experiments (25-30%):

• Training scripts run within Azure ML workspaces: The students should have the expertise in creating and running experiments utilizing Azure ML SDK as well configuring run settings for the scripts. This subject area also requires their skills in data consumption from datasets for an experiment using Azure ML SDK.
• Model training process automation: The individuals need the relevant skills in running pipelines, passing data within steps in pipelines, monitoring pipeline runs, and creating pipelines with the use of SDK.
• Metrics generation from experiment runs: The candidates must be able to use logs for troubleshooting errors in experiment runs, log metrics from experiment run, and view and retrieve experiment outputs.
• Models creation with Azure ML Designer: This domain covers the examinees’ skills in using custom code modules within the design and using designer modules for the definition of pipeline data flows. It also requires one’s competence in ingesting data within designer pipelines and creating training pipelines utilizing ML Designer.

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