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| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI solutions | 15-20% | - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Implement model monitoring and feedback - Deploy and manage generative models |
| Plan and manage an Azure AI solution | 20-25% | - Plan solutions aligned with responsible AI principles - Select suitable AI models - Select appropriate Microsoft Foundry Services - Monitor, optimize, and secure AI solutions - Create and configure Azure AI resources - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining |
| Implement computer vision solutions | 10-15% | - Process and index video content - Analyze images and detect objects/features - Integrate vision capabilities into applications - Extract text and handwriting from images - Build and deploy custom vision models |
| Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Implement intelligent search and retrieval - Build knowledge bases and search indexes - Extract entities, relationships, and key phrases |
| Implement natural language processing solutions | 15-20% | - Implement translation and summarization - Customize and deploy NLP models - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots |
| Implement an agentic solution | 5-10% | - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types - Test, deploy, and optimize agents |
我々の目標はAI-102試験に準備するあなたに試験に合格させることです。この目標を実現するようには、我が社のPass4Testは試験改革のとともにめざましく推進していき、最も専門的なAI-102問題集をリリースしています。現時点で我々のMicrosoft AI-102問題集を使用しているあなたは試験にうまくパースできると信じられます。心配なく我々の真題を利用してください。
質問 # 313
You are building a retail chatbot that will use a QnA Maker service.
You upload an internal support document to train the model. The document contains the following Question;
"What is your warranty period?"
Users report that the chatbot returns the default QnA Maker answer when they ask the following Question;
"How long is the warranty coverage?"
The chatbot returns the correct answer when the users ask the following Question;
'What is your warranty period?"
Both questions should return the same answer.
You need to increase the accuracy of the chatbot responses.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. (Choose three.)
正解：
解説：
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/edit-knowledge-base
質問 # 314
You are building a chatbot for a Microsoft Teams channel by using the Microsoft Bot Framework SDK. The chatbot will use the following code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation
Box 1: Yes
The ActivityHandler.OnMembersAddedAsync method overrides this in a derived class to provide logic for when members other than the bot join the conversation, such as your bot's welcome logic.
Box 2: Yes
membersAdded is a list of all the members added to the conversation, as described by the conversation update activity.
Box 3: No
Reference:
https://docs.microsoft.com/en-us/dotnet/api/microsoft.bot.builder.activityhandler.onmembersaddedasync?view=b
質問 # 315
You are planning the product creation project.
You need to recommend a process for analyzing videos.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. (Choose four.)
正解：
解説：
Reference:
https://azure.microsoft.com/en-us/blog/get-video-insights-in-even-more-languages/
https://docs.microsoft.com/en-us/azure/media-services/video-indexer/video-indexer-output-json-v2
質問 # 316
You are developing an internet-based training solution for remote learners.
Your company identifies that during the training, some learners leave their desk for long periods or become distracted.
You need to use a video and audio feed from each learner's computer to detect whether the learner is present and paying attention. The solution must minimize development effort and identify each learner.
Which Azure Cognitive Services service should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Scenario Recap
You are building a remote training monitoring solution.
Requirement: Use video and audio feeds to detect if a learner is present, paying attention, and talking.
Services available: Face, Speech, Text Analytics.
Analysis
From a learner's video feed, verify whether the learner is present.
The Face API can detect and identify faces in a video feed.
It can tell if a person is present and recognized, fulfilling the requirement.
From a learner's facial expression in the video feed, verify whether the learner is paying attention.
Again, the Face API provides facial expression and emotion recognition (happiness, anger, neutral, etc.).
This can be mapped to "paying attention vs. distracted."
From a learner's audio feed, detect whether the learner is talking.
The Speech service detects spoken input and can determine if speech is present.
Text Analytics works on text (not raw audio) and is therefore not appropriate here.
Final Answer (Answer Area Selections)
From a learner's video feed, verify whether the learner is present: Face From a learner's facial expression in the video feed, verify whether the learner is paying attention: Face From a learner's audio feed, detect whether the learner is talking: Speech Microsoft References Face API - Face detection & identification Face API - Emotion recognition Azure Speech service
質問 # 317
You need to upload speech samples to a Speech Studio project. How should you upload the samples?
正解：A
解説：
To upload your data, navigate to the Speech Studio . From the portal, click Upload data to launch the wizard and create your first dataset. You'll be asked to select a speech data type for your dataset, before allowing you to upload your data.
The default audio streaming format is WAV
Use this table to ensure that your audio files are formatted correctly for use with Custom Speech:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/how-to-custom-speech-test-and-train
質問 # 318
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