Exam Topics
The potential candidates for this certification test must develop their skills in its topics before attempting it. It is recommended to go through the official webpage to review the detailed objectives. Microsoft updates the topics of its exams regularly, so studying the latest domains is very important to your success in the test. Thus, Microsoft AI-900 covers the following sections:
Describe AI Considerations and Workloads (15-20%)
The first topic evaluates the following skills:
- Identify the common AI Workloads features – You should be able to identify a variety of workloads, such as forecasting/prediction, computer vision, and conversational AI. The subsection also covers the characteristics of anomaly detection workloads, knowledge mining, and natural language processing workloads.
- Identify the guiding principles associated with responsible Artificial Intelligence – This one will evaluate the candidates’ ability to explain some concepts. These include considerations of safety and reliability in AI solutions and considerations for fairness in AI solutions. It also covers considerations for security and privacy, transparency, inclusiveness, and accountability within an AI solution.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
How to book the AI-900: Microsoft Azure AI Fundamentals Exam
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- Step 2: Sign up/Login to Pearson VUE account.
- Step 3: Select local centre based on your country, date, time and confirm with a payment method.
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Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
| Describe fundamental principles of machine learning on Azure | 30-35% | - Describe features of no-code automated ML - Describe core machine learning concepts - Identify common machine learning tasks - Describe Azure Machine Learning capabilities |
| Describe features of Generative AI workloads on Azure | 15-20% | - Describe generative AI concepts - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI |
| Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify Azure AI services for computer vision - Identify common computer vision tasks |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Describe Azure capabilities for NLP - Identify common NLP tasks |

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