PDF Version Demo4. 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.
- 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.
- 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.
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Microsoft Designing and Implementing a Data Science Solution on Azure Exam Certification Details:
| Exam Name | Microsoft Certified - Azure Data Scientist Associate |
| Exam Price | $165 (USD) |
| Books / Training | DP-100T01-A: Designing and Implementing a Data Science Solution on Azure |
| Sample Questions | Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions |
| Exam Code | DP-100 |
| Passing Score | 700 / 1000 |
| Duration | 120 mins |
| Schedule Exam | Pearson VUE |
| Number of Questions | 40-60 |
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Microsoft DP-100 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Topic 2: Design and prepare a machine learning solution | - Manage compute and data assets - Plan and configure Azure Machine Learning workspace - Select appropriate Azure services for machine learning workloads |
| Topic 3: Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
| Topic 4: Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
| Topic 5: Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
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