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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Passing Score: | 700 / 1000 |
| Exam Price: | USD 99 |
| Exam Format: | Match the service to use case, Drag and drop, Multiple choice, Scenario-based, Multiple select |
| Available Languages: | Indonesian (Indonesia), Chinese (Traditional), Spanish, French, Italian, Arabic (Saudi Arabia), German, Chinese (Simplified), Russian, English, Japanese, Korean, Portuguese (Brazil) |
| Real Exam Qty: | 40-60 |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals |
| Exam Duration: | 45-60 |
| Certificate Validity Period: | 1 year (renewable via free online assessment on Microsoft Learn) |
| Sample Questions: | Microsoft AI-901 Sample Questions |
| Exam Way: | Online (remote proctored via Pearson VUE) or at a Pearson VUE test center |
| Pre Condition: | No prerequisites. This exam is intended for both technical and non-technical backgrounds. Data science and software engineering experience are not required. Awareness of basic cloud concepts and client-server applications is beneficial. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900 |
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NEW QUESTION # 11
Which two scenarios are examples of a natural language processing workload? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
The Smart Home Device: It uses speech-to-text to understand voice commands, parses grammatical structures to find user intent, and generates a spoken answer using text-to-speech.
The Website Chatbot: It processes incoming typed text, matches user phrases to topics in a structured knowledge base, and serves human-like conversational text answers back to the browser.
NEW QUESTION # 12
Drag and Drop Question
You have a Microsoft Foundry project that contains a model deployment.
You are developing an application that sends an image and a user question to the model.
You need to send both text and image content in the same request so the model can return an answer.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 13
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The completed sentence is:
Ensuring that human reviewers oversee AI-generated decisions and remain responsible for the final output is an example of the Microsoft responsible AI principle of accountability .
Accountability means people and organizations remain responsible for AI systems and their effects. Human review and responsibility for final decisions are examples of accountability.
The other options are incorrect:
fairness focuses on avoiding bias and treating people fairly.
privacy and security focuses on protecting data and restricting access.
transparency focuses on explaining AI use, capabilities, and limitations.
NEW QUESTION # 14
You need to build an AI solution that produces new product images based on written descriptions provided by users. Which AI workload should you use?
Answer: C
Explanation:
An AI workload that creates entirely new visual content based on written natural language descriptions is defined as image generation, which is a core capability of Generative AI. In the Microsoft Azure ecosystem, this task is primarily handled by generative models like DALL-E or GPT-image via Azure OpenAI Service and Azure AI Foundry.
Reference:
https://learn.microsoft.com/en-us/azure/architecture/data-guide/ai-services/image-video-processing
NEW QUESTION # 15
What should you use to compare benchmark metrics of different AI models?
Answer: B
Explanation:
To compare benchmark metrics across Azure AI models, navigate to the Azure AI Foundry portal, explore the model catalog, and use the benchmarking tools to assess model performance. You can then compare these metrics to choose the most suitable model for your needs.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/benchmark-model-in-catalog
NEW QUESTION # 16
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