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3. Models Management and Optimization (20-25%):
- 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.
- Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.
- 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.
- 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.
Obligatory Prerequisites
Officially, there is no prior work-experience or educational expertise related to DP-100 exam. Anyone, willing to make it big in the world of data science, can go for it. However, industry pundits say that beginner-level expertise in concepts like running data experiments and machine learning will make the exam journey a lot more simplified and easy to accomplish.
Who should take the DP-100 exam
The Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam certification is an internationally-recognized validation that identifies persons who earn it as possessing skilled as a Microsoft Certified Azure Data Scientist Associate. If candidates want significant improvement in career growth needs enhanced knowledge, skills, and talents. The Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam certification provides proof of this advanced knowledge and skill. If a candidate has knowledge of associated technologies and skills that are required to pass Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam then he should take this exam.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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 Price: | $165 USD |
| Exam Duration: | 100 minutes |
| Passing Score: | 700 |
| Available Languages: | Indonesian (Indonesia), Korean, English, Arabic (Saudi Arabia), Japanese, French, Chinese (Traditional), German, Portuguese (Brazil), Chinese (Simplified), Spanish, Italian, Russian |
| Related Certifications: | Microsoft Certified: Azure Data Engineer Associate Microsoft Certified: Azure AI Engineer Associate |
| Certificate Validity Period: | 1 year |
| Exam Format: | Drag and drop, Multiple select, Multiple choice, Case studies, Yes/No |
| Real Exam Qty: | 40-60 |
| Recommended Training: | Course DP-100T01-A: Designing and Implementing a Data Science Solution on Azure Microsoft Learn Learning Path |
| 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 |
Microsoft DP-100日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Optimize language models for AI applications | 25-30% | - Optimize with Retrieval Augmented Generation
|
| Topic 2: Design and prepare a machine learning solution | 20-25% | - Design a machine learning solution
|
| Topic 3: Explore data and run experiments | 20-25% | - Run experiments
|
| Topic 4: Train and deploy models | 25-30% | - Train models
|
By Mortimer
