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| Section | Weight | Objectives |
|---|---|---|
| Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Implement Azure AI Search solutions - Implement semantic and vector search - Design document processing pipelines - Create custom skills for Azure AI Search |
| Implement Computer Vision Solutions | 15-20% | - Process video and generate insights - Analyze images using Azure AI Vision - Read and process documents using Azure AI Document Intelligence - Implement object detection and image classification - Implement Azure AI Face service |
| Implement Generative AI Solutions | 10-15% | - Use Azure OpenAI Service for generative AI - Implement prompt engineering techniques - Apply responsible AI practices - Integrate Azure OpenAI models into applications |
| Implement Decision Support Solutions | 10-15% | - Monitor and optimize decision support solutions - Design and implement decision automation - Integrate Azure AI services into applications |
| Plan and Manage an Azure AI Solution | 15-20% | - Implement governance and compliance for AI solutions - Manage AI costs and resource allocation - Select appropriate Azure AI services and technologies - Plan and configure secure AI solutions |
| Implement Natural Language Processing Solutions | 20-25% | - Build question answering solutions - Create conversational AI solutions with Azure AI Bot Service - Translate text and speech - Implement speech capabilities using Azure AI Speech - Analyze text using Azure AI Language |
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NEW QUESTION # 225
You are building a Chatbot by using the Microsoft Bot Framework SDK. The bot will be used to accept food orders from customers and allow the customers to customize each food item. You need to configure the bot to ask the user for additional input based on the type of item ordered. The solution must minimize development effort. Which two types of dialogs should you use? Each correct answer presents part of the solution. NOTE:
Each correct selection is worth one point.
Answer: A,E
Explanation:
* In the Bot Framework SDK, if you want to collect structured input step by step, you typically use a Waterfall dialog. Each step in the waterfall represents one piece of logic, such as asking for the size, toppings, or type of food item.
* Prompts are used inside waterfall steps to actually ask the user for input (e.g., text prompt, choice prompt, date-time prompt).
* Adaptive, Action, Input are concepts more relevant to Power Virtual Agents or Adaptive Dialogs (Composer), not the Bot Framework SDK question context.
* Since the question specifically mentions Bot Framework SDK, the correct answer is Waterfall dialog + Prompts.
The answer: C, D
NEW QUESTION # 226
You develop an application that uses the Face API.
You need to add multiple images to a person group.
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: Stream
The File.OpenRead(String) method opens an existing file for reading.
Example: Open the stream and read it back.
using (FileStream fs = File.OpenRead(path))
Box 2: AddFaceFromStreamAsync
Step 5 on
https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/how-to-add-faces
NEW QUESTION # 227
You have an Azure subscription
You need to create a new resource that will generate fictional stories in response to user prompts. The solution must ensure that the resource uses a customer-managed key to protect data.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
To create a resource that can generate fictional stories from prompts, you need an Azure OpenAI account (-- kind OpenAI).
To protect data with a customer-managed key (CMK), you must pass the key information (Key Vault URI, key name, key version) via the --encryption parameter when creating the Cognitive Services account. The JSON shown (with keyName, keyVersion, and keyVaultUri) is exactly the payload expected by --encryption.
A complete example pattern:
az cognitiveservices account create \
-n myresource -g myResourceGroup -l westeurope \
--kind OpenAI --sku S \
--assign-identity \
--encryption '{
"keyName":"keyname",
"keyVersion":"secretVersion",
"keyVaultUri":"https://<your-kv>.vault.azure.net/"
}'
(Adding --assign-identity allows the service to access the Key Vault key; the hotspot specifically asks for the parameter that takes the JSON block and the resource kind, which are --encryption and OpenAI.) Microsoft References
* Azure CLI: Create Cognitive Services account with customer-managed keys using --encryption.
https://learn.microsoft.com/azure/ai-services/cognitive-services-encryption-keys#configure-customer- managed-keys
* Azure Cognitive Services (Azure AI services) CLI parameters including --kind OpenAI.https://learn.
microsoft.com/azure/ai-services/cognitive-services-management-api-cli#create-a-cognitive-services- account
NEW QUESTION # 228
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:
Explanation:
NEW QUESTION # 229
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:
Explanation:
You are deploying a Language Understanding (LUIS) container. When running LUIS in Docker, the model must first be exported from the Language Understanding portal, and then provided to the container at runtime.
Step-by-step reasoning:
From the Language Understanding portal, export the solution as a package file.
The trained LUIS model must be exported from the portal into a .json package file.
This is required because the container cannot access the hosted service directly.
From the host computer, move the package file to the Docker input directory.
Containers expect the model to be available locally.
The package file is placed into the input directory that the container maps for models.
From the host computer, run the container and specify the input directory.
When starting the container, you specify --volume < local_input > :/input so the container has access to the package file.
This makes the exported model available inside the container for processing.
Why not the other options?
Retain the model in the portal is not sufficient; the container cannot pull directly from the cloud.
Build the container and specify the output directory is not required; the container image is already available from MCR and is not custom-built for this step.
Correct Answer Order:
Export the solution as a package file.
Move the package file to the Docker input directory.
Run the container and specify the input directory.
Microsoft References
Run LUIS containers
Use containers with Azure AI services
NEW QUESTION # 230
......
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