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| Section | Objectives |
|---|---|
| Topic 1: Implement Knowledge Mining and Document Intelligence | - Build Azure AI Search solutions - Enrich and index data for search scenarios - Implement document intelligence solutions |
| Topic 2: Implement Computer Vision Solutions | - Analyze images and videos using Azure AI Vision - Implement custom vision models - Extract information using OCR and form recognition |
| Topic 3: Implement Generative AI Solutions | - Implement Azure OpenAI Service solutions - Build retrieval-augmented generation (RAG) solutions - Integrate prompt engineering techniques |
| Topic 4: Implement Natural Language Processing Solutions | - Translate and interpret language using Azure AI services - Analyze text using Azure AI Language - Implement question answering and conversational AI |
| Topic 5: Plan and Manage an Azure AI Solution | - Plan solution requirements and architecture - Manage security, compliance, and responsible AI - Select appropriate Azure AI services |
>> AI-102 Valid Exam Answers <<
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NEW QUESTION # 212
Drag and Drop Question
You are building a Language Understanding model for purchasing tickets.
You have the following utterance for an intent named PurchaseAndSendTickets.
Purchase [2 audit business] tickets to [Paris] [next Monday] and send tickets to
[email@domain.com]
You need to select the entity types. The solution must use built-in entity types to minimize training data whenever possible.
Which entity type should you use for each label? To answer, drag the appropriate entity types to the correct labels. Each entity type 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.
Answer:
Explanation:
Explanation:
Box 1: GeographyV2
The prebuilt geographyV2 entity detects places. Because this entity is already trained, you do not need to add example utterances containing GeographyV2 to the application intents.
Box 2: Email
Email prebuilt entity for a LUIS app: Email extraction includes the entire email address from an utterance. Because this entity is already trained, you do not need to add example utterances containing email to the application intents.
Box 3: Machine learned
The machine-learning entity is the preferred entity for building LUIS applications.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt- geographyv2
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-email
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/reference-entity-machine-learned- entity
NEW QUESTION # 213
You are building an agent by using the Azure Al 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:
NEW QUESTION # 214
You have a Custom Vision resource named acvdev in a development environment.
You have a Custom Vision resource named acvprod in a production environment.
In acvdev, you build an object detection model named obj1 in a project named proj1.
You need to move obj1 to acvprod.
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.
Answer:
Explanation:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-tutorial-pattern#what-did-this-tutorial-accomp
NEW QUESTION # 215
Hotspot Question
You are building an agent by using the Azure AI 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:
Box 1: BingGroundingTool
Grounding with Bing Search allows your Azure AI Agents to incorporate real-time public web data when generating responses. You need to create a Grounding with Bing Search resource, and then connect this resource to your Azure AI Agents. When a user sends a query, Azure AI Agents decide if Grounding with Bing Search should be leveraged or not. If so, it will leverage Bing to search over public web data and return relevant chunks. Lastly, Azure AI Agents will use returned chunks to generate a response.
Box 2: tools=
Create an Agent with the Grounding with Bing search tool enabled
To make the Grounding with Bing search tool available to your agent, use a connection to initialize the tool and attach it to the agent.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/agents/how-to/tools/bing-grounding
https://learn.microsoft.com/en-us/azure/ai-services/agents/how-to/tools/bing-code- samples?pivots=python
NEW QUESTION # 216
You are building a Conversational Language Understanding model.
You need to ensure that the model will support the following sample utterances:
* Set all the lights to on.
* Turn off the lights in the living room.
* What is the current thermostat temperature?
* Lower the temperature of the thermostat by five degrees.
Which three elements should you add to the model?
Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Answer: A,D,E
NEW QUESTION # 217
......
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