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

Microsoft DP-100 Exam Syllabus Topics:

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

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Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam

Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam which is related to Microsoft Certified Azure Data Scientist Associate Certification. This DP-100 exam validates the ability to apply scientific rigor and data exploration techniques to gain actionable insights and communicate results to stakeholders. This DP-100 exam also tests the Candidate knowledge to use machine learning techniques to train, evaluate, and deploy models to build AI solutions that satisfy business objectives. Candidates must have skills to use applications that involve natural language processing, speech, computer vision, and predictive analytics. Azure Data Scientist usually hold or pursue this certification and you can expect the same job role after completion of this certification.

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

• 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.
• 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.
• 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.
• 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.