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| Section | Weight | Objectives |
|---|---|---|
| Plan and manage an Azure AI solution | 20-25% | - Select suitable AI models - Select appropriate Microsoft Foundry Services - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources - Monitor, optimize, and secure AI solutions - Plan solutions aligned with responsible AI principles |
| Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Customize and deploy NLP models - Implement translation and summarization - Build conversational AI and chatbots |
| Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Integrate Azure OpenAI and other generative models - Deploy and manage generative models |
| Implement an agentic solution | 5-10% | - Develop multi-agent workflows and orchestration - Test, deploy, and optimize agents - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service |
| Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Implement intelligent search and retrieval - Ingest and process structured/unstructured data |
| Implement computer vision solutions | 10-15% | - Process and index video content - Integrate vision capabilities into applications - Analyze images and detect objects/features - Build and deploy custom vision models - Extract text and handwriting from images |
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질문 # 70
You are building a chatbot.
You need to use the Content Moderator API to identify aggressive and sexually explicit language.
Which three settings should you configure? To answer, select the appropriate settings in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
To detect aggressive language (e.g., profanity/abuse) and sexually explicit content in text using the Content Moderator Text - Screen API:
classify = true
Enables machine-assisted text classification that returns category scores (e.g., sexually explicit/suggestive categories). This is required to identify sexual content.
language = < code > (for example, eng )
Specifies the language so profanity/abusive terms are detected correctly. The profanity detection in Text- Screen is language dependent.
autocorrect = true
Helps normalize intentionally misspelled or obfuscated words so that profanity or aggressive terms are still detected (e.g., "f@#$" # "fuck").
Other parameters in the blade are not necessary for this goal:
PII targets personal data extraction, not aggression/sexuality.
listId is for matching against custom blocklists.
Headers like Ocp-Apim-Subscription-Key and Content-Type are required to call the API, but they are not the content-analysis settings that control detection behavior asked for in this question.
Microsoft Azure AI References
Azure AI Content Moderator - Screen text API parameters (autocorrect, language, classify) and category outputs.
Azure AI Content Moderator - Text moderation concepts: profanity detection and classification categories.
질문 # 71
You have an Azure subscription that contains an Azure Al Content Safety resource named CS1.
You create a test image that contains a circle.
You submit the test image to CS1 by using the curl command and the following command-line parameters.
What should you expect as the output?
정답：A,C
질문 # 72
You use the Microsoft Bot Framework Composer to build a chatbot that enables users to purchase items.
You need to ensure that the users can cancel in-progress transactions. The solution must minimize development effort.
What should you add to the bot?
정답：A
설명：
An interruption occurs when a trigger is fired. Context of adaptive dialogs.
질문 # 73
You have a chatbot that uses a QnA Maker application.
You enable active learning for the knowledge base used by the QnA Maker application.
You need to integrate user input into the model.
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.
정답：
설명：
Explanation:
Step 1: For the knowledge base, select Show active learning suggestions.
In order to see the suggested questions, on the Edit knowledge base page, select View Options, then select Show active learning suggestions.
Step 2: Approve and reject suggestions.
Each QnA pair suggests the new question alternatives with a check mark, , to accept the question or an x to reject the suggestions. Select the check mark to add the question.
Step 3: Save and train the knowledge base.
Select Save and Train to save the changes to the knowledge base.
Step 4: Publish the knowledge base.
Select Publish to allow the changes to be available from the GenerateAnswer API.
When 5 or more similar queries are clustered, every 30 minutes, QnA Maker suggests the alternate questions for you to accept or reject.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/improve-knowledge-base
질문 # 74
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation
질문 # 75
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