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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.
The Microsoft DP-100 test helps candidates check their machine learning knowledge and skills and prove themselves as qualified data scientists.
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Exam DP-100: Designing and Implementing a Data Science Solution on Azure
Candidates for the Azure Data Scientist Associate certification should have subject matter expertise applying data science and machine learning to implement and run machine learning workloads on Azure.
Responsibilities for this role include planning and creating a suitable working environment for data science workloads on Azure. You run data experiments and train predictive models. In addition, you manage, optimize, and deploy machine learning models into production.
A candidate for this certification should have knowledge and experience in data science and using Azure Machine Learning and Azure Databricks.
Part of the requirements for: Microsoft Certified: Azure Data Scientist Associate
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Microsoft DP-100日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Optimize language models for AI applications | 25-30% | - Implement generative AI solutions
|
| Explore data and run experiments | 20-25% | - Run experiments
|
| Design and prepare a machine learning solution | 20-25% | - Manage Azure Machine Learning workspace
|
| Train and deploy models | 25-30% | - Train models
|
