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Microsoft doesn’t provide detailed information regarding its exams, including the DP-100 certification test. The total number of questions in exam is not published on the official website, but the candidates can expect to encounter about 40-60 items in their delivery of their test. There is also a high probability that they will face scenario-based questions with single-answer and multiple-answer options. The duration is all about 120 minutes. The exam is available in several languages, including Japanese, English, Simplified Chinese, and Korean.
The Microsoft DP-100 exam can be taken at any nearest Pearson VUE testing center or online as a proctored option. The cost for the exam is $165 in the United States but the price is usually different in other countries due to taxation. To find out the actual pricing for your country, simply check the official webpage.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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.
Books is another efficient preparation tool. There are several guides available for the Microsoft DP-100 exam but the most popular are listed below:
| Section | Objectives |
|---|---|
| Topic 1: Design and prepare a machine learning solution | - Plan and configure Azure Machine Learning workspace - Select appropriate Azure services for machine learning workloads - Manage compute and data assets |
| Topic 2: Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
| Topic 3: Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
| Topic 4: Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Topic 5: Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
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