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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.
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Testing Process Details of Microsoft DP-100
As for the exam format, there are usually about 40-60 multiple-choice, active screen, best answer, hot area, build lists, labs, mark review and other question styles. You are expected to complete them in 3 hours. The passing score is 700 out of 1000 maximum possible points. The test is available in the following languages: English, Japanese, Korean, and Chinese (Simplified). To schedule your test, you should first pay the registration fee of $165 and go to the Pearson VUE platform to create an account. Once you log in, choose the “Proctored Exams” section and enter ‘DP-100' as the exam code. Follow the prompts to complete registration. At this stage, you can choose to take your test offline or online. Notice, though, that if you don't succeed on the first try, you'll be required to pay the enrollment fee for the next attempt as well.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Step 4: Training with Practice Tests
Practicing more and more before taking the exam can help you score better. Using practice tests, you can also significantly improve your time management skills. You can easily find the relevant practice questions on different online learning platforms.
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Microsoft DP-100日本語 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Design and prepare a machine learning solution | - Plan and configure Azure Machine Learning workspace - Manage compute and data assets - Select appropriate Azure services for machine learning workloads |
| Topic 2: Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
| Topic 3: Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
| Topic 4: Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
| Topic 5: Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |

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