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Key Details
Any individual who is planning to pass the Microsoft AI-100 exam should understand its details. The first thing that you need to know about this test is that the students will be given 180 minutes to answer 62 questions. This certification exam consists of various types of questions, including multiple choice, scenario-based, correct sequence, and drag and drop, among others. To complete the test, the applicants should get a minimum score of 700 or more on a scale of 100-1000. The exam is available in various languages, such as Korean, Simplified Chinese, Japanese, and English. To schedule it, you will have to pay $165 as a registration fee.
It is very important that you know the retake policy for the Microsoft exams because there is a high chance that you will need it. You will only have a total of five attempts to pass the test in a single year. If you fail the first time, you will have to wait 24 hours before you can retake the exam. But if you fail the test during your second attempt, you will wait for 14 days before you can have the third, fourth, and fifth attempts. As far as canceling or rescheduling the exam is concerned, it is crucial to do so at least 24 hours prior to the test otherwise you will forfeit your fee.
Passing the Microsoft AI-100: Designing and Implementing an Azure AI Solution exam is required for anyone who wants to earn the Microsoft Certified: Azure AI Engineer Associate certification. This test evaluates the ability of the candidates to perform the following technical tasks: analyzing solution requirements; designing solutions; integrating AI models into solutions; and deploying and handling solutions.
Reference: https://www.microsoft.com/en-us/learning/exam-ai-100.aspx
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Microsoft AI-100日本語 Exam Syllabus Topics:
| Section | Objectives |
| Topic 1: Implement Knowledge Mining and Search Solutions | - Azure Cognitive Search
- 1. Indexing and querying data
- 2. Skillsets and enrichment pipeline
|
| Topic 2: Implement Computer Vision Solutions | - Image analysis and processing
- 1. Custom Vision training and deployment
- 2. Computer Vision API usage
|
| Topic 3: Design AI Solutions on Azure | - Design conversational AI solutions
- 1. Language understanding (LUIS concepts)
- 2. Azure Bot Service architecture
- Select appropriate Azure AI services
- 1. Knowledge mining solutions
- 2. Azure Cognitive Services selection
- 3. Azure Machine Learning integration
|
| Topic 4: Implement Natural Language Processing (NLP) | - Text analytics solutions
- 1. Key phrase extraction
- 2. Sentiment analysis
- Language understanding solutions
- 1. Intent recognition
- 2. Entity extraction
|
| Topic 5: Responsible AI and Monitoring | - AI ethics and governance
- 1. Responsible AI principles
- 2. Model monitoring and evaluation
|