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| Section | Objectives |
|---|---|
| Topic 1: Plan and Manage an Azure AI Solution | - Plan solution requirements and architecture - Select appropriate Azure AI services - Manage security, compliance, and responsible AI |
| Topic 2: Implement Natural Language Processing Solutions | - Implement question answering and conversational AI - Translate and interpret language using Azure AI services - Analyze text using Azure AI Language |
| Topic 3: Implement Knowledge Mining and Document Intelligence | - Implement document intelligence solutions - Build Azure AI Search solutions - Enrich and index data for search scenarios |
| Topic 4: Implement Computer Vision Solutions | - Extract information using OCR and form recognition - Analyze images and videos using Azure AI Vision - Implement custom vision models |
| Topic 5: Implement Generative AI Solutions | - Build retrieval-augmented generation (RAG) solutions - Integrate prompt engineering techniques - Implement Azure OpenAI Service solutions |
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NEW QUESTION # 390
You are building a natural language model.
You need to enable active learning.
What should you do?
Answer: B
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-how-to-review-endpoint-utterances#log-user-queries-to-enable-active-learning
NEW QUESTION # 391
SIMULATION
You need to create and publish a bot that will use Language Understanding and QnA Maker. The bot must be named bot12345678. You must publish the bot by using the User1-
12345678@abc.com account.
NOTE: Complete this task first. It may take several minutes to complete the required deployment steps. While this is taking place, you can complete tasks 2-6 in this lab during the deployment.
To complete this task, use the Microsoft Bot Framework Composer.
Answer:
Explanation:
Step 1: Sign in to the QnAMaker.ai portal with your Azure credentials. Use the User1-
12345678@abc.com account
Step 2: Publish the knowledge base. In the QnA Maker portal, select Publish. Then to confirm, select Publish on the page.
The QnA Maker service is now successfully published. You can use the endpoint in your application or bot code.
Step 3: In the QnA Maker portal, on the Publish page, select Create bot.
This button appears only after you've published the knowledge base.
After publishing the knowledge base, you can create a bot from the Publish page.
Step 4: A new browser tab opens for the Azure portal, with the Azure Bot Service's creation page.
Configure the Azure bot service.
Bot name: bot12345678
The bot will be created.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/quickstarts/create-publish- knowledge-base
NEW QUESTION # 392
You have a monitoring solution that uses the Azure Al Anomaly Detector service.
You provision a server named Server! that has intermittent internet access.
You need to deploy the Azure Al Anomaly Detector to Server 1.
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:
NEW QUESTION # 393
You have the following C# function.
static void MyFunction(TextAnalyticsClient textAnalyticsClient, string text) var response = textAnalyticsClient.ExtractKeyPhrases(text); Console.WriteLine("Key phrases:"); foreach (string keyphrase in response.Value) Console.WriteLine($"{keyphrase)");
}
You call the function by using the following code.
MyFunction(textAnalyticsClient, "the quick brown fox jumps over the lazy dog"); Following 'Key phrases', what output will you receive?
Answer: D
Explanation:
To determine the output of the provided C# function when calling MyFunction with the text "the quick brown fox jumps over the lazy dog", we need to understand the behavior of the ExtractKeyPhrases method from the Azure Text Analytics SDK.
The ExtractKeyPhrases method is designed to extract important phrases from a given text. These key phrases typically represent the main subjects or entities within the text.
Given the text "the quick brown fox jumps over the lazy dog," the likely key phrases extracted by the Azure Text Analytics service would be the main entities or noun phrases.
NEW QUESTION # 394
You are developing an application that includes language translation.
The application will translate text retrieved by using a function named get_text_to_be_translated. The text can be in one of many languages. The content of the text must remain within the Americas Azure geography.
You need to develop code to translate the text to a single language.
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:
API Endpoint Selection
The constraint states that the content of the text must remain within the Americas Azure geography .
" api.cognitive.microsofttranslator.com " : This is the global endpoint , which doesn ' t guarantee data residency in a specific geography.
" api-apc.cognitive.microsofttranslator.com " : This endpoint refers to the Asia Pacific geography .
" api-nam.cognitive.microsofttranslator.com " : This endpoint refers to the North and South America (NAM) geography .
Therefore, to meet the data residency requirement for the Americas, you must select " api-nam.cognitive.
microsofttranslator.com " .
2. Path/Query String Selection
The requirement is to translate the text to a single language . The input text can be in " one of many languages, " meaning the source language isn ' t fixed and should be detected automatically. The language to translate to is implied to be English ( en ) based on the options available and common translation scenarios.
" /translate?from=en " : This is incorrect because it specifies the source language as English, which contradicts the " text can be in one of many languages " requirement (implying automatic source language detection).
" /translate?suggestedFrom=en " : This is not the standard way to initiate a translation request and seems to suggest a source language, which is unnecessary if auto-detection is desired/possible.
" /translate?to=en " : This is the correct and standard way to request a translation where the source language is automatically detected , and the target language is English ( en ) . This fulfills the translation requirement.
" /detect?to=en " / " /detect?from=en " : These endpoints are used for language detection and not for the translation itself. The prompt explicitly asks to develop code to translate the text.
NEW QUESTION # 395
......
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