Designing and Implementing a Data Science Solution on Azure (DP-100日本語版) training material
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Basic Exam Traits
The Microsoft DP-100 is an associate-level job role-based exam. Its structure is the same as any other exam falling in this category. As per the standard test format, DP-100 exam is likely to contain 40-60 exam questions. As far as question format is concerned, Microsoft doesn’t follow a set pattern. The exam is likely to cover questions based on the MCQ pattern. However, the odds of including items based on other patterns like case studies and best answers are also high. What’s more, there is no exact passing score as there is no fixed number of questions and it might change as per the final number of tasks. Nonetheless, a test-taker must secure 70% passing marks to be called successful in the official exam. Currently, this test can be taken in English, Japanese, Chinese (Simplified), and Korean worldwide. The standard exam fee is $165 and is likely to get changed as per the location of the test-taker.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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Microsoft DP-100: Requirements
The Microsoft DP-100 exam is created for the individuals who are involved in implementing machine learning techniques. The candidates for this certification test should have an in-depth knowledge of identifying and preparing the development environment, applying scientific data and rigor exploration techniques to acquire actionable insights along with communicating results to the stakeholders. They should also possess the skills in preparing data for modeling and developing models. In addition, they should also be able to apply machine learning techniques to train, deploy and evaluate models to build any AI solutions, which satisfy the business objectives.
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Microsoft DP-100日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Design and prepare a machine learning solution | 20-25% | - Prepare development environments
|
| Prepare a model for deployment | 20-25% | - Deploy machine learning models
|
| Explore data and train models | 35-40% | - Prepare data for modeling
|
| Deploy and retrain models | 10-15% | - Implement retraining pipelines
|





