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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement AI solutions by using Microsoft Foundry | 55-60% | - Implement AI solutions
|
| Topic 2: Identify AI concepts and responsibilities | 40-45% | - Describe principles of responsible AI
|
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NEW QUESTION # 122
Hotspot Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 123
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: B
Explanation:
A Generative AI workload is exactly what should be used for this scenario. Generative AI models (such as LLMs) are specifically designed to interpret prompts and create original, context-aware content, making them the perfect fit for drafting tailored marketing emails based on a product description and target audience.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry/tools/content-understanding
NEW QUESTION # 124
Hotspot Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Yes
Yes, image analysis capabilities in Azure Vision in Foundry Tools can perform optical character recognition (OCR). The built-in Read OCR engine extracts both printed and handwritten text in multiple languages and writing styles from images and documents.
Box 2: Yes
Yes, image analysis capabilities in Azure Vision in Foundry Tools can automatically generate concise one-sentence human-readable captions and descriptive content tags for thousands of recognizable objects, living beings, actions, and settings.
Box 3: No
Image analysis capabilities in Foundry Tools are designed to process, interpret, and extract insights from existing images, rather than create new ones.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview-image-analysis
NEW QUESTION # 125
You have 100 instructional videos that do NOT contain any audio. Each instructional video has a script.
You need to generate a narration audio file for each video based on the script.
Which type of workload should you use?
Answer: C
Explanation:
Text to speech enables your applications, tools, or devices to convert text into humanlike synthesized speech. The text to speech capability is also known as speech synthesis. Use humanlike prebuilt neural voices out of the box, or create a custom neural voice that's unique to your product or brand.
Reference:
https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/text-to-speech
NEW QUESTION # 126
Drag and Drop Question
Match the description to the corresponding Microsoft Foundry tools.
To answer, drag the appropriate description from the column on the left to its tool on the right.
Each description may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Transcribes spoken input
Speech recognition in Microsoft Foundry transcribes spoken input into text using built-in speech- to-text capabilities.
Transcription FeaturesReal-time transcription: Converts microphone inputs or live audio streams into text instantly.
Batch transcription: Processes pre-recorded audio or video files asynchronously.
Advanced audio models: Leverages tools like GPT-transcribe and GPT-live-transcribe to handle natural speech patterns, numbers, addresses, and industry-specific terminology.
Broad language support: Handles over 140 locales and languages for spoken input.
Box 2: Generates spoken output
Speech synthesis in Microsoft Foundry (via Azure Speech in Foundry Tools) converts text input into realistic, human-like spoken audio output.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/speech-service/speech-to-text
NEW QUESTION # 127
......
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