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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals (AI-900) Exam |
| Exam Number: | AI-900 |
| Certificate Validity Period: | Does not expire (Fundamentals certification) |
| Real Exam Qty: | 40-60 |
| Available Languages: | German, Portuguese (Brazil), Korean, Japanese, Chinese (Traditional), Chinese (Simplified), French, English, Spanish |
| Exam Format: | Multiple response, Case study (limited), Multiple choice, Drag and drop |
| Exam Duration: | 60 minutes |
| Passing Score: | 700 (out of 1000) |
| Exam Price: | Approx. 99 USD (varies by region) |
| Related Certifications: | Microsoft Azure Fundamentals (AZ-900) Microsoft Azure Data Fundamentals (DP-900) |
| Recommended Training: | Microsoft Learn - AI-900 Learning Path Azure AI Fundamentals Course |
| Exam Registration: | Schedule exam via Pearson VUE Microsoft Certification Portal |
| Sample Questions: | Microsoft AI-901 Sample Questions |
| Exam Way: | Online proctored exam or in-person test center |
| Pre Condition: | No formal prerequisites required. Basic understanding of cloud computing and AI concepts is recommended. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/ |
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NEW QUESTION # 125
You need to build an AI solution that generates marketing email drafts based on a short description of a product and its target audience.
Which AI workload should you use?
Answer: A
Explanation:
Generating marketing email drafts from a short product description and target audience is a content generation task. This is a generative AI workload because the system creates new text based on the user's prompt.
B . computer vision is for interpreting images or video.
C . text classification categorizes existing text, but does not draft new marketing emails.
D . speech recognition converts spoken audio into text.
Therefore, the correct answer is A. generative AI.
NEW QUESTION # 126
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
When using the OpenAI Responses API and a vision-enabled model, you can include an image in a request by providing the image as a base64-encoded image data .
Azure OpenAI / Microsoft Foundry vision requests support image input as an input_image item. The image can be provided by URL or as base64-encoded image data in the request. Microsoft documentation for Azure OpenAI batch examples also lists Base64 encoded image and Image URL as supported input patterns for Responses API requests.
The other options are incorrect:
a CSV file attachment is not an image input format for vision analysis.
an MP4 video stream is video content, not an image format.
a shared access signature (SAS) token is only an access token for a protected resource; it is not the image data itself.
NEW QUESTION # 127
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:
Explanation:
input=[
{
" role " : " user " ,
" content " : [
{ " type " : " input_text " , " text " : " What is in this image? Provide 3 bullet points. " },
{ " type " : " input_image " , " image_url " : image_url},
],
}
]
print(response.output_text)
For a multimodal request that sends both text and an image to an Azure OpenAI model, the input content array uses separate content item types. Text content is sent with type: " input_text " , and image content is sent with type: " input_image " . Microsoft's Azure OpenAI Responses API documentation shows the Responses API supports multimodal inputs and uses structured input content items in the request.
The options output_text and output_image are not used inside the user input content array. In the shown code, response.output_text is used after the response is returned to print the model's text answer.
NEW QUESTION # 128
Which action can be performed by using the Azure Vision in Foundry Tools service?
Answer: A
Explanation:
Azure Vision in Foundry Tools supports OCR capabilities for reading printed and handwritten text from images and documents. Microsoft documentation states that OCR extracts text from images and documents, including handwritten text.
Therefore, extracting data from handwritten letters is the action that can be performed by using Azure Vision in Foundry Tools.
NEW QUESTION # 129
You have a Microsoft Foundry project that has a generative AI model deployment.
You need to ensure that responses generated by the model minimize costs and remain within a defined length.
Which parameter should you configure?
Answer: D
NEW QUESTION # 130
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