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The dream of becoming a highly skilled data scientist can turn into a reality with the help of the Microsoft DP-100 exam. This exam tries to impart an associate-level understanding of data science and machine learning with an aim to generate a skilled workforce of data scientists.
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Exam Details & Skills Measured
Microsoft DP-100 is an associate-level certification exam. It can be taken as a proctored test if this option is available in your country. If it’s not, you can sit for it as an online exam at one of the Pearson VUE testing centers across the world. You should check the official webpage for more information about scheduling this exam.
When you are ready for this test, you have to pay $165 as a registration fee, which applies to a single delivery of the exam. You can take it in English, Korean, Japanese, or Simplified Chinese. Microsoft DP-100 contains 40-60 questions that cover different types, such as drag and drop, multiple choice, active screen, build list, short answer, best answer, and case studies, among others. If you want to pass this test on the first try, you should get 720 points on a scale of 100-1000.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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
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Microsoft DP-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy and retrain models | 10-15% | - Monitor deployed models
|
| Topic 2: Design and prepare a machine learning solution | 20-25% | - Prepare development environments
|
| Topic 3: Explore data and train models | 35-40% | - Prepare data for modeling
|
| Topic 4: Prepare a model for deployment | 20-25% | - Deploy machine learning models
|
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