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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement Generative AI Solutions | 10-15% | - Apply responsible AI practices - Integrate Azure OpenAI models into applications - Implement prompt engineering techniques - Use Azure OpenAI Service for generative AI |
| Topic 2: Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Implement Azure AI Search solutions - Design document processing pipelines - Implement semantic and vector search - Create custom skills for Azure AI Search |
| Topic 3: Implement Computer Vision Solutions | 15-20% | - Implement object detection and image classification - Analyze images using Azure AI Vision - Implement Azure AI Face service - Process video and generate insights - Read and process documents using Azure AI Document Intelligence |
| Topic 4: Implement Decision Support Solutions | 10-15% | - Design and implement decision automation - Integrate Azure AI services into applications - Monitor and optimize decision support solutions |
| Topic 5: Implement Natural Language Processing Solutions | 20-25% | - Analyze text using Azure AI Language - Implement speech capabilities using Azure AI Speech - Translate text and speech - Build question answering solutions - Create conversational AI solutions with Azure AI Bot Service |
| Topic 6: Plan and Manage an Azure AI Solution | 15-20% | - Implement governance and compliance for AI solutions - Plan and configure secure AI solutions - Manage AI costs and resource allocation - Select appropriate Azure AI services and technologies |
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問題 #257
You are building a chatbot by using the Microsoft Bot Framework Composer as shown in the exhibit. (Click the Exhibit tab.)
The chatbot contains a dialog named GetUserDetails. GetUserDetails contains a TextInput control that prompts users for their name.
The user input will be stored in a property named name.
You need to ensure that you can dispose of the property when the last active dialog ends.
Which scope should you assign to name?
答案：B
解題說明：
The dialog scope associates properties with the active dialog. Properties in the dialog scope are retained until the dialog ends.
Incorrect Answers:
A: The conversation scope associates properties with the current conversation. Properties in the conversation scope have a lifetime of the conversation itself. These properties are in scope while the bot is processing an activity associated with the conversation (for example, multiple users together in a Microsoft Teams channel).
B: The user scope associates properties with the current user. Properties in the user scope do not expire.
These properties are in scope while the bot is processing an activity associated with the user.
C: The turn scope associates properties with the current turn. Properties in the turn scope expire at the end of the turn.
Reference:
https://docs.microsoft.com/en-us/composer/concept-memory?tabs=v2x
問題 #258
You have an Azure subscription.
You plan to build a solution That will analyze scanned documents and export relevant fields to a database.
You need to recommend which Azure Al service to deploy for the following types of documents:
* Internal expenditure request authorization forms
* Supplier invoices
The solution must minimize development effort.
What should you recommend for each document type? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
答案：
解題說明：
Explanation:
問題 #259
You are developing an internet-based training solution for remote learners.
Your company identifies that during the training, some learners leave their desk for long periods or become distracted.
You need to use a video and audio feed from each learner's computer to detect whether the learner is present and paying attention. The solution must minimize development effort and identify each learner.
Which Azure Cognitive Services service should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/what-are-cognitive-services
問題 #260
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You create a web app named app1 that runs on an Azure virtual machine named vm1. Vm1 is on an Azure virtual network named vnet1.
You plan to create a new Azure Cognitive Search service named service1.
You need to ensure that app1 can connect directly to service1 without routing traffic over the public internet.
Solution: You deploy service1 and a public endpoint to a new virtual network, and you configure Azure Private Link.
Does this meet the goal?
答案：B
解題說明：
* You have a web app app1 running on VM1 in vnet1.
* You plan to create an Azure Cognitive Search service (service1).
* Goal: Ensure app1 can connect to service1 without routing over the public internet.
Scenario Recap
Proposed Solution"You deploy service1 and a public endpoint to a new virtual network, and you configure Azure Private Link."
* Azure Cognitive Search and Private Link:
* Azure Cognitive Search supports Private Endpoints via Azure Private Link.
* When you configure a private endpoint, the service is accessible privately over the virtual network and traffic does not go over the public internet.
* You can still keep a public endpoint enabled optionally, but access from your VM can be restricted to the private endpoint.
* Key Point: The solution specifies using a public endpoint AND a private link.
* This still meets the requirement because Private Link ensures that VM1 in vnet1 communicates with service1 privately.
* The existence of the public endpoint does not force traffic to go public - traffic from the private endpoint is routed over the VNet.
Analysis
ConclusionYes, this solution meets the goal because Private Link provides secure, private connectivity to Cognitive Search, avoiding the public internet.
The answer: A. Yes
* Secure access to Azure Cognitive Search with Private Link
* Azure Private Link overview
Microsoft References
問題 #261
You have an Azure subscription that is linked to a Microsoft Entra tenant. The subscription ID is c7ac27a8-d186-2326-8509-c3762dad06c9 and the tenant ID is 8c4e1aab-1a2c-a2e1-ae39-e5e6e23106c9.
The subscription contains an Azure OpenAl resource named OpenAl1 that has a primary API key of
1363c794d19e430baf27a9a5f29c42ab. OpenAl1 has a deployment named embeddings1 that uses the text-embedding-ada-002 model.
You need to query OpenAl1 and retrieve embeddings for text input.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
To complete the code to query OpenAI and retrieve embeddings for text input, let's review the provided image and fill in the blanks appropriately.
Here's the full code with the necessary selections based on the provided image:
Uri endpoint = new Uri("https://openai.openai.azure.com");
AzureKeyCredential credentials = new AzureKeyCredential("1363c794d19e430baf27a9a5f29c42ab"); OpenAIClient openAIClient = new OpenAIClient(endpoint, credentials); EmbeddingsOptions embeddingOptions = new EmbeddingsOptions("your text input here"); var returnValue = openAIClient.GetEmbeddings("embeddings1", embeddingOptions); foreach (float item in returnValue.Value.Data[0].Embedding)
{
Console.WriteLine(item);
}
* Uri endpoint: This is set to the URI of your Azure OpenAI resource.
* AzureKeyCredential credentials: This uses the primary API key of the Azure OpenAI resource.
* OpenAIClient openAIClient: This initializes the OpenAIClient with the endpoint and credentials.
* EmbeddingsOptions embeddingOptions: This initializes the EmbeddingsOptions with the text input for which you want to retrieve embeddings.
* GetEmbeddings method: This method is used to query the embeddings model. The first parameter is the deployment name ("embeddings1"), and the second parameter is the embedding options that include the text input.
The filled-in code should look like this:
* Uri endpoint: "https://openai.openai.azure.com"
* AzureKeyCredential credentials: "1363c794d19e430baf27a9a5f29c42ab"
* OpenAIClient openAIClient: new OpenAIClient(endpoint, credentials)
* EmbeddingsOptions embeddingOptions: new EmbeddingsOptions("your text input here")
* GetEmbeddings method call: openAIClient.GetEmbeddings("embeddings1", embeddingOptions) With these selections, the code is complete and ready to query the Azure OpenAI resource to retrieve embeddings for the given text input.
問題 #262
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