- Exam Code: AI-100
- Exam Name: Designing and Implementing an Azure AI Solution
- Certification Provider: Microsoft
- Corresponding Certification:Azure AI Engineer Associate
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Passing the Microsoft AI-100 exam and obtaining the corresponding certification will prove to be of great help in advancing your career. After successfully completing this test, you will get loads of career proposals. It will be easier for you to get a decent job and earn a much higher salary. Here are some of the positions you can apply for:
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Reference: https://www.microsoft.com/en-us/learning/exam-ai-100.aspx
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Skills Measured
There are various topics that you will need to study to achieve success in the Microsoft AI-100 exam. All the domains included in this certification tests are as follows:
The first subject area that you will have to master is called Analyzing Solution Requirements. It makes up 25-30% of the exam content and consists of the following subdomains: recommending Azure Cognitive Services APIs to satisfy business requirements; mapping security requirements to tools, technologies, as well as processes; selecting the software, services, and storage necessary for supporting a solution.
The next section that you have to study is Designing an AI Solution that constitutes about 40-45% of the exam questions. It revolves around the subtopics, such as designing solutions that comprise one or more pipelines; designing solutions that utilize Cognitive Services; designing solutions that implement the Microsoft Bot Framework; designing the compute infrastructure to support a solution; designing for data governance, compliance, integrity, as well as security.
The last domain included in the Microsoft AI-100 exam is titled Implementing & Monitoring AI Solutions. It makes up the remaining 25-30% of the whole content. The subtopics covered here are the following: implementing an AI workflow; integrating AI services & solution elements; monitoring and assessing the AI environment.
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Microsoft AI-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Design AI Solutions | 40-45% | - Design knowledge mining and search solutions - Integrate open-source and custom AI components - Design natural language processing solutions - Design conversational AI and bot solutions - Design computer vision solutions - Select appropriate Azure Cognitive Services |
| Analyze Solution Requirements | 25-30% | - Identify business and technical requirements - Define solution performance and scalability criteria - Evaluate data storage and processing options - Assess security, compliance, and governance needs |
| Implement and Monitor AI Solutions | 25-30% | - Deploy AI solutions to Azure infrastructure - Configure security and access controls - Integrate AI models and services into applications - Maintain and update AI solutions - Monitor performance, usage, and health |
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