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>> AI-901 Free Sample Questions <<
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NEW QUESTION # 96
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Named Entity Recognition (NER) is used to extract dates, quantities, and locations from text.
Named Entity Recognition identifies and categorizes entities in text, such as people, organizations, locations, dates, times, quantities, currencies, and other structured information.
The other options are incorrect:
Key phrase extraction identifies important phrases or main topics.
Language detection identifies the language of the text.
Sentiment Analysis identifies positive, negative, neutral, or mixed sentiment.
NEW QUESTION # 97
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: No
No, batch transcription in Azure Speech does not use a microphone. It processes large volumes of prerecorded audio files stored in locations like Azure Blob Storage.
Box 2: No
No, Azure Speech in Foundry Tools cannot analyze video frames to detect or label objects. It is strictly an audio-focused tool designed for speech-to-text transcription, text-to-speech synthesis, speech translation, and voice analytics Box 3: No No, speech translation in Azure Speech in Foundry Tools outputs translated text as well as optional spoken audio.
Supported Output Formats
Text output: It can output the recognized speech as translated text in one or multiple target languages without generating any spoken audio.
Speech-to-Speech: It can optionally synthesize that translated text into natural-sounding audible speech (audio output) using text-to-speech capabilities.
Source transcription: It simultaneously provides the original source language transcription in text format alongside the translation results.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/speech-service/batch-transcription
https://learn.microsoft.com/en-us/azure/foundry/responsible-ai/speech-service/speech-to-text/transparency-note
NEW QUESTION # 98
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: D
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 # 99
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: B
Explanation:
To minimize cost and keep generated responses within a defined length, configure Max Completion Tokens.
Microsoft's Azure OpenAI / Foundry API reference defines max_completion_tokens as an upper bound for the number of tokens that can be generated for a completion. Because generated tokens contribute to usage and response length, limiting completion tokens helps control both output length and cost.
Temperature and Top P control randomness or sampling behavior, not maximum response length. Model version settings do not directly define the generated response length.
NEW QUESTION # 100
What should you use to identify similar faces in a set of images?
Answer: D
Explanation:
Correct:
* Azure AI Vision
Incorrect:
* Azure AI Custom Vision
* Azure AI Language
* Azure OpenAI Service
* Azure OpenAI in Foundry Models
Note: Azure AI Foundry, Vision, Find similar faces
To identify similar faces across different images using Azure, the Azure AI Face service is the most suitable choice. This service leverages AI algorithms to detect, recognize, and analyze human faces, including features like face grouping based on visual similarity.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/how-to/find-similar-faces
NEW QUESTION # 101
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