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Microsoft AI-901 Exam Syllabus Topics:
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Reference: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-901
Microsoft Azure AI Fundamentals Sample Questions:
Verifying that machine learning models do NOT show racial or gender bias is an example of which Microsoft responsible AI principle?
- A. Reliability
- B. fairness
- C. Privacy and security
- D. Safety
Correct Answer: B π³οΈ
Explanation: Only visible for PassTestking members. You can sign-up / login (it's free).
You have a website that includes customer reviews.
You need to store the reviews in English and present the reviews to users in their respective language based on each user ' s geographical location.
Which type of natural language processing workload should you use?
- A. translation
- B. speech recognition
- C. key phrase extraction
- D. language modeling
Correct Answer: A π³οΈ
You have a Microsoft Foundry project that contains an agent named Agent1.
You need to ensure that Agent1 always calls an Azure function when the agent responds to user input.
To what should you set tool_choice for Agent1?
- A. none
- B. auto
- C. required
Correct Answer: C π³οΈ
Explanation: Only visible for PassTestking members. You can sign-up / login (it's free).
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:

Explanation:
Statement 1: In the Foundry playground, you can upload a local image and include text in the same message when prompting a multimodal model. = Yes Microsoft documentation for vision-enabled chat models states that in the chat session pane, you can select the attachment button, upload an image, add a text prompt such as "Describe this image," and then submit it.
Statement 2: When using the OpenAI Responses API and a vision-enabled model, images can be provided only as base64-encoded image data. = No Images are not limited to base64 data. The Responses API supports image input through an image URL, and Azure OpenAI documentation also describes examples using base64 encoded image data.
Statement 3: Prompts that include images require deploying a text-only model because multimodal capabilities are handled by the application layer. = No Image prompts require a vision-enabled multimodal model, not a text-only model. Azure OpenAI vision- capable models accept image and text inputs directly.
For each of the following statements, select if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:

Explanation:
Statement
Answer
Image analysis capabilities in Microsoft Foundry Tools can perform optical character recognition (OCR).
Yes
Image analysis capabilities in Microsoft Foundry Tools can generate captions and descriptive tags for images.
Yes
Image analysis capabilities in Microsoft Foundry Tools are designed primarily to create new images from text prompts.
No
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide
/topics:
The correct sequence is Yes, Yes, No.
The first statement is Yes. Azure Vision Image Analysis in Microsoft Foundry Tools supports optical character recognition (OCR). Microsoft documents Image Analysis 4.0 as supporting synchronous OCR through its Read capability, which extracts printed or handwritten text contained within images.
The second statement is also Yes. Image Analysis provides both captioning and image tagging. Captions generate a human-readable description of image content, while tags identify recognizable objects, scenes, actions, and other visual concepts. Microsoft explicitly lists capabilities such as Captions, Dense Captions, Tags, Object Detection, People detection, and Read/OCR as Image Analysis features.
The third statement is No. Image Analysis is primarily concerned with extracting information from existing images, not generating new images. Creating an image from a textual prompt is a generative AI/image- generation workload, typically performed by image-generation models. Image Analysis instead interprets visual input by extracting text, captions, tags, objects, and related information.
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