Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Candidates for the Azure Data Scientist Associate certification should have subject matter expertise applying data science and machine learning to implement and run machine learning workloads on Azure.

Responsibilities for this role include planning and creating a suitable working environment for data science workloads on Azure. You run data experiments and train predictive models. In addition, you manage, optimize, and deploy machine learning models into production.

A candidate for this certification should have knowledge and experience in data science and using Azure Machine Learning and Azure Databricks.

Part of the requirements for: Microsoft Certified: Azure Data Scientist Associate

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

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

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

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The DP-100 exam is a hands-on practical exam that requires the candidate to demonstrate their ability to design and implement data science solutions on Azure. DP-100 exam includes various tasks, such as creating machine learning models, building data pipelines, and deploying models to production environments. The tasks are designed to simulate real-world scenarios that data scientists and engineers face in their jobs.

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