What is the duration of the DP-100 Exam
- Length of Examination: 150 minutes
- Format: Multiple choices, multiple answers
- Passing Score: 700/1000
- Number of Questions: 45-60
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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What Certificate You Will Get by Passing DP-100
DP-100 syllabus includes concepts of machine learning workloads, handling data experiments, optimizing and managing models, and many more. This is an associate-level test that creates a strong base for candidates' future professional development. This Microsoft exam is associated with the Microsoft Certified: Azure Data Scientist Associate certification. This is the only test that one has to ace to become accredited and is considered the best choice as it has no formal prerequisites & allows specialists to validate their proficiency in utilizing Azure Machine Learning Service and many other related solutions.
What Next after This Certification?
The certification obtained from passing DP-100 exam is proof of intermediate skills in data science since it is within the associate hierarchy. Apart from applying for and getting a job in a preferred company, you can also expand your skills in an area of interest. With the role-based scheme, there are expert certifications awaiting you to earn. They include the Microsoft Certified: Azure Solutions Architect Expert and the Microsoft Certified: Azure DevOps Engineer Expert.
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DP-100 Exam Target Audience
Candidates with permission to take DP-100 exam are those aspiring to become Azure data scientists and have formed huge knowledge in both data science and machine learning workloads based on Azure.
Microsoft DP-100 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
| Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
| Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
| Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| 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 |
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