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Main Prerequisites
Before you decide to take the Microsoft AI-100 exam, it is important that you understand all the knowledge requirements for this test. The exam is not designed for the general public, so you need to possess certain competence before attempting it. The target candidates for this certification test should have the following skills:
- Understanding when a custom API should be developed to satisfy certain business requirements.
- Familiarity with data storage in Azure, Azure Cognitive Search, Microsoft Azure Cognitive Services, and Azure Bot Service.
- Expertise in implementing and designing AI applications and agents that utilize Microsoft Azure Cognitive Services.
- Ability to put forward solutions that utilize open source technologies with understanding the elements that make up the Azure AI portfolio along with the available data storage options.
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Languages: English, Japanese, Chinese (Simplified), Korean
This exam measures your ability to accomplish the following technical tasks: analyze solution requirements; design solutions; integrate AI models into solutions; and deploy and manage solutions.
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How much AI-100 Exam Cost
The price of the AI-100 exam is $165 USD.
Reference: https://www.microsoft.com/en-us/learning/exam-ai-100.aspx
Career Path
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:
- AI Engineer
- AI Strategy Architect
- Customer Engineer
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Microsoft AI-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement and Monitor AI Solutions | 25-30% | - Deploy AI solutions to Azure infrastructure - Monitor performance, usage, and health - Maintain and update AI solutions - Integrate AI models and services into applications - Configure security and access controls |
| Topic 2: Analyze Solution Requirements | 25-30% | - Evaluate data storage and processing options - Identify business and technical requirements - Define solution performance and scalability criteria - Assess security, compliance, and governance needs |
| Topic 3: Design AI Solutions | 40-45% | - Design computer vision solutions - Design natural language processing solutions - Design knowledge mining and search solutions - Select appropriate Azure Cognitive Services - Design conversational AI and bot solutions - Integrate open-source and custom AI components |






