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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement Generative AI Solutions | 10-15% | - Implement prompt engineering techniques - Apply responsible AI practices - Use Azure OpenAI Service for generative AI - Integrate Azure OpenAI models into applications |
| Topic 2: Implement Decision Support Solutions | 10-15% | - Design and implement decision automation - Monitor and optimize decision support solutions - Integrate Azure AI services into applications |
| Topic 3: Implement Natural Language Processing Solutions | 20-25% | - Analyze text using Azure AI Language - Create conversational AI solutions with Azure AI Bot Service - Translate text and speech - Build question answering solutions - Implement speech capabilities using Azure AI Speech |
| Topic 4: Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Create custom skills for Azure AI Search - Implement semantic and vector search - Design document processing pipelines - Implement Azure AI Search solutions |
| Topic 5: Plan and Manage an Azure AI Solution | 15-20% | - Plan and configure secure AI solutions - Implement governance and compliance for AI solutions - Manage AI costs and resource allocation - Select appropriate Azure AI services and technologies |
| 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 # 103
You are developing a streaming Speech to Text solution that will use the Speech SDK and MP3 encoding.
You need to develop a method to convert speech to text for streaming MP3 data.
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:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/how-to-use-codec-compressed-audio-input-streams?tabs=debian&pivots=programming-language-csharp
NEW QUESTION # 104
Drag and Drop Question
You are building a transcription service for technical podcasts.
Testing reveals that the service fails to transcribe technical terms accurately.
You need to improve the accuracy of the service.
Which five 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:
NEW QUESTION # 105
You plan to build a chatbot to support task tracking.
You create a Conversational Language Understanding service named Iu1.
You need to build a Conversational Language Understanding model to Integrate into the chatbot. The solution must minimize development time to build 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.
Answer:
Explanation:
Explanation:
Correct Sequence:
Add the prebuilt domain ToDo
Add a new application
Train the application
Publish the application
Comprehensive Detailed Explanation
You are building a Conversational Language Understanding (CLU) model for a chatbot that supports task tracking. To minimize development time, the best approach is to use a prebuilt domain (like ToDo) instead of building intents/entities manually.
Steps in correct order:
Add the prebuilt domain ToDo
Prebuilt domains in CLU provide ready-to-use intents and entities for common scenarios (e.g., calendar, email, ToDo).
This significantly reduces development time since you don't need to create all intents/entities manually.
Add a new application
You must first create a CLU application that uses the prebuilt domain.
The application acts as the container for intents, entities, and utterances.
Train the application
Training is required after adding intents, entities, and utterances (or using a prebuilt domain).
This ensures the model can classify and extract information correctly.
Publish the application
Once trained, you must publish the application so it can be accessed via an endpoint and integrated into the chatbot.
Incorrect Actions in this Case
Add example utterances # Not required if you're using the prebuilt ToDo domain (utterances are already included).
Correct Sequence:
Add the prebuilt domain ToDo
Add a new application
Train the application
Publish the application
Microsoft References
Conversational Language Understanding (CLU) overview
Prebuilt domains in CLU (LUIS ToDo example)
Train and publish CLU models
NEW QUESTION # 106
You are developing a call to the Face API. The call must find similar faces from an existing list named employeefaces. The employeefaces list contains 60,000 images.
How should you complete the body of the HTTP request? To answer, drag the appropriate values to the correct targets. Each value 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.
Answer:
Explanation:
Explanation:
You are calling the Face API to find similar faces from an existing list called employeefaces.
Key facts from the scenario:
* The list contains 60,000 images.
* In Face API:
* faceListId # limited to 1,000 faces.
* largeFaceListId # designed for very large lists, up to 1,000,000 faces.
Since employeefaces has 60,000 images, you must use LargeFaceListId, not faceListId.
* "faceId": "18c51a87-3a69-47a8-aedc-a54745f708a1"
* This is the face to search for.
* "largeFaceListId": "employeefaces"
* Must use LargeFaceListId because the list has 60,000 images.
* "maxNumOfCandidatesReturned": 1
* Limits the number of matches returned.
* "mode": "matchFace"
* Two options exist:
* "matchFace" # returns the most similar individual faces.
* "matchPerson" # groups faces by person.
* Since the requirement is find similar faces, use "matchFace".
Parameters in the Request Body
Completed Answer{
"faceId": "18c51a87-3a69-47a8-aedc-a54745f708a1",
"largeFaceListId": "employeefaces",
"maxNumOfCandidatesReturned": 1,
"mode": "matchFace"
}
* LargeFaceListId # "employeefaces"
* matchFace # "mode"
Correct Values to Drag and Drop
Final Answer:
* First blank: LargeFaceListId
* Second blank: matchFace
* Face API - Find Similar
* Face API - Large Face List operations
Microsoft References
NEW QUESTION # 107
You have a Language Understanding solution that runs in a Docker container.
You download the Language Understanding container image from the Microsoft Container Registry (MCR).
You need to deploy the container image to a host computer.
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:
1 - From the Language Understanding portal, export the solution as a package file.
2 - From the host computer, move the package file to the docker input directory.
3 - From the host computer, build the container and specify the output directory.
NEW QUESTION # 108
......
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