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  1. Home
  2. Microsoft Certification
  3. AI-100 Exam
  4. Microsoft.AI-100.v2026-07-27.q119 Dumps
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Question 81

You are designing an Azure Batch Al solution that will be used to train many different Azure Machine Learning models. The solution will perform the following:
* Image recognition
* Deep learning that uses convolutional neural networks
You need to select a compute infrastructure for each model. The solution must minimize the processing time.
What should you use for each model? To answer, drag the appropriate compute infrastructures to the correct models. Each compute infrastructure 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.

Correct Answer:

Explanation:
References:
https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu
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Question 82

You are designing an AI solution that will provide feedback to teachers who train students over the Internet. The students will be in classrooms located in remote areas. The solution will capture video and audio data of the students in the classrooms.
You need to recommend Azure Cognitive Services for the AI solution to meet the following requirements:
Alert teachers if a student seems angry or distracted.
Identify each student in the classrooms for attendance purposes.
Allow the teachers to log the text of conversations between themselves and the students.
Which Cognitive Services should you recommend?

Correct Answer: E
Azure Video Indexer is a cloud application built on Azure Media Analytics, Azure Search, Cognitive Services (such as the Face API, Microsoft Translator, the Computer Vision API, and Custom Speech Service). It enables you to extract the insights from your videos using Video Indexer video and audio models.
Face API enables you to search, identify, and match faces in your private repository of up to 1 million people.
The Face API now integrates emotion recognition, returning the confidence across a set of emotions for each face in the image such as anger, contempt, disgust, fear, happiness, neutral, sadness, and surprise.
These emotions are understood to be cross-culturally and universally communicated with particular facial expressions.
Speech-to-text from Azure Speech Services, also known as speech-to-text, enables real-time transcription of audio streams into text that your applications, tools, or devices can consume, display, and take action on as command input. This service is powered by the same recognition technology that Microsoft uses for Cortana and Office products, and works seamlessly with the translation and text-to- speech.
Incorrect Answers:
Computer Vision or the QnA is not required.
References:
https://docs.microsoft.com/en-us/azure/media-services/video-indexer/video-indexer-overview
https://azure.microsoft.com/en-us/services/cognitive-services/face/
https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/speech-to-text
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Question 83

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an app named App1 that uses the Face API.
App1 contains several PersonGroup objects.
You discover that a PersonGroup object for an individual named Ben Smith cannot accept additional entries.
The PersonGroup object for Ben Smith contains 10,000 entries.
You need to ensure that additional entries can be added to the PersonGroup object for Ben Smith. The solution must ensure that Ben Smith can be identified by all the entries.
Solution: You create a second PersonGroup object for Ben Smith.
Does this meet the goal?

Correct Answer: B
Instead, use a LargePersonGroup. LargePersonGroup and LargeFaceList are collectively referred to as large- scale operations. LargePersonGroup can contain up to 1 million persons, each with a maximum of 248 faces.
LargeFaceList can contain up to 1 million faces. The large-scale operations are similar to the conventional PersonGroup and FaceList but have some differences because of the new architecture.
References:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/how-to-use-large-scale
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Question 84

DRAG DROP
You need to build a pipeline for an Azure Machine Learning experiment.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

Correct Answer:
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Question 85

You deploy an Azure bot.
You need to collect Key Performance Indicator (KPI) data from the bot. The type of data includes:
* The number of users interacting with the bot
* The number of messages interacting with the bot
* The number of messages on different channels received by the bot
* The number of users and messages continuously interacting with the bot What should you configure?

Correct Answer: A
Explanation
References:
https://docs.microsoft.com/en-us/azure/sql-database/saas-multitenantdb-adhoc-reporting
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