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Microsoft DP-100日本語 Exam Overview:
| Certification Vendor: | Microsoft |
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Exam Number: | DP-100 |
| Exam Duration: | 100 minutes |
| Real Exam Qty: | 40-60 |
| Passing Score: | 700 |
| Available Languages: | English, Japanese, Chinese (Simplified), Korean, German, Chinese (Traditional), French, Spanish, Portuguese (Brazil), Italian, Russian, Arabic (Saudi Arabia), Indonesian (Indonesia) |
| Related Certifications: | Microsoft Certified: Azure Data Engineer Associate Microsoft Certified: Azure AI Engineer Associate |
| Exam Format: | Multiple choice, Multiple select, Drag and drop, Case studies, Yes/No |
| Certificate Validity Period: | 1 year |
| Exam Price: | $165 USD |
| Recommended Training: | Microsoft Learn Learning Path Course DP-100T01-A: Designing and Implementing a Data Science Solution on Azure |
| Exam Registration: | Microsoft Learn Registration Pearson VUE Scheduling |
| Sample Questions: | Microsoft DP-100日本語 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No mandatory prerequisites; recommended knowledge: Azure fundamentals, Python programming, data science concepts, machine learning frameworks (Scikit-learn, PyTorch, Tensorflow) |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-100 |
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.
- Production computes targets creation: The test takers should perform their skills in compute options evaluation for deployment and consideration of security for deployed services.
- 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.
3. Models Management and Optimization (20-25%):
- Usage of Automated Machine Learning for the creation of optimal models: This section requires your skills in retrieving the best models and getting data for Automated ML runs. It also covers competence in defining primary metrics, selecting pre-processing alternatives, and determining the algorithms to be searched. The candidates should be able to use the Automated Machine Learning from Azure ML SDK as well as Automated ML interface within Azure ML studios.
- Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.
- Usage of hyperdrive for the tuning of hyperparameters: This domain will evaluate the ability of the applicants to define search space, primary metrics, and early termination alternatives. It also expects their skills in sampling techniques selection and model discovery that require optimal hyper-parameter values.
- Usage of model explainers for the interpretation of models: The learners have to demonstrate their competence in choosing model interpreters and generating the features of important data.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Microsoft DP-100日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Train and deploy models | 25-30% | - Manage models
|
| Topic 2: Optimize language models for AI applications | 25-30% | - Implement generative AI solutions
|
| Topic 3: Design and prepare a machine learning solution | 20-25% | - Design a machine learning solution
|
| Topic 4: Explore data and run experiments | 20-25% | - Implement pipelines
|
By Kelly
