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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement Natural Language Processing Solutions | 20-25% | - Create conversational AI solutions with Azure AI Bot Service - Translate text and speech - Build question answering solutions - Analyze text using Azure AI Language - Implement speech capabilities using Azure AI Speech |
| Topic 2: Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Implement semantic and vector search - Design document processing pipelines - Create custom skills for Azure AI Search - Implement Azure AI Search solutions |
| Topic 3: Plan and Manage an Azure AI Solution | 15-20% | - Plan and configure secure AI solutions - Select appropriate Azure AI services and technologies - Manage AI costs and resource allocation - Implement governance and compliance for AI solutions |
| Topic 4: Implement Decision Support Solutions | 10-15% | - Integrate Azure AI services into applications - Design and implement decision automation - Monitor and optimize decision support solutions |
| Topic 5: Implement Generative AI Solutions | 10-15% | - Integrate Azure OpenAI models into applications - Apply responsible AI practices - Implement prompt engineering techniques - Use Azure OpenAI Service for generative AI |
| Topic 6: Implement Computer Vision Solutions | 15-20% | - Read and process documents using Azure AI Document Intelligence - Analyze images using Azure AI Vision - Process video and generate insights - Implement object detection and image classification - Implement Azure AI Face service |
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NEW QUESTION # 104
You need to measure the public perception of your brand on social media messages. Which Azure Cognitive Services service should you use?
Answer: B
Explanation:
Text Analytics Cognitive Service could be used to quickly determine the public perception for a specific topic, event or brand.
Example: A NodeJS app which pulls Tweets from Twitter using the Twitter API based on a specified search term. Then pass these onto Text Analytics for sentiment scoring before storing the data and building a visualisation in PowerBI. The Architecture looked something like this:
Reference:
https://www.linkedin.com/pulse/measuring-public-perception-azure-cognitive-services-steve-dalai
NEW QUESTION # 105
You have a blog that allows users to append feedback comments. Some of the feedback comments contain harmful content that includes discriminatory language.
You need to create a prototype of a solution that will detect the harmful content. The solution must minimize development effort.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,E
NEW QUESTION # 106
You have a chatbot.
You need to test the bot by using the Bot Framework Emulator. The solution must ensure that you are prompted for credentials when you sign in to the bot.
Which three settings should you configure? To answer, select the appropriate settings in the answer area.
NOTE Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 107
You build a Language Understanding model by using the Language Understanding portal.
You export the model as a JSON file as shown in the following sample.
To what does the Weather.Historic entity correspond in the utterance?
Answer: D
Explanation:
Let's carefully analyze the JSON output from the exported LUIS (Language Understanding) model.
The utterance is:
"average amount of rain by month at chicago last year"
The extracted entities are:
* Weather.WeatherRange # "average" (positions 0-6)
* Weather.WeatherCondition # "rain" (positions 18-21)
* Weather.Historic # "by month" (positions 23-30)
We need to determine what text fragment corresponds to Weather.Historic.
Looking at the "startPos": 23, "endPos": 30, these indexes map to the substring "by month" in the utterance.
* B. chicago # Refers to a location entity, but the entity name is Weather.Location (not shown here).
* C. rain # Already captured as Weather.WeatherCondition.
* D. location # Would also be "chicago," but again that's a different entity type.
So the only correct match for Weather.Historic is "by month".
The answer: A. by month
* Entities in LUIS
* Export LUIS model schema
* Labeling utterances with entities
NEW QUESTION # 108
You are building an agent by using the Azure Al Foundry Agent Service.
You need to ensure that the agent can access publicly accessible data that was released during the past 90 days.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
To let an agent use public web data with recency controls (e.g., limit to content from the last 90 days), you add the Bing grounding tool. In the Azure AI Foundry Agent Service SDK, you create a connection (e.g., search_connection_id ) and then construct a BingGroundingTool with that connection. The tool exposes a list of definitions (tool operations) that the agent can call at runtime.
When creating the agent, you pass tool definitions via the tools= argument (a list of ToolDefinition ). The tool_resources= argument is used when the tool requires additional resource descriptors (e.g., Azure AI Search index configuration). For Bing grounding, providing the connection and passing the definitions as tools= is sufficient. This enables the agent to call Bing with date filters to retrieve only recent, publicly accessible content.
Microsoft Azure AI References (titles only)
Azure AI Agent Service - Bing web grounding tool
Azure AI Agent Service - Create an agent (agent creation parameters including tools and tool_resources ) Azure AI Agent Service - Tool connections and definitions
NEW QUESTION # 109
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