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| Section | Weight | Objectives |
|---|---|---|
| Implement natural language processing solutions | 15-20% | - Customize and deploy NLP models - Build conversational AI and chatbots - Implement translation and summarization - Perform text analysis, sentiment detection, and language detection |
| Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Integrate vision capabilities into applications - Build and deploy custom vision models - Process and index video content - Extract text and handwriting from images |
| Plan and manage an Azure AI solution | 20-25% | - Plan solutions aligned with responsible AI principles - Monitor, optimize, and secure AI solutions - Select appropriate Microsoft Foundry Services - Create and configure Azure AI resources - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Select suitable AI models |
| Implement generative AI solutions | 15-20% | - Deploy and manage generative models - Implement model monitoring and feedback - Integrate Azure OpenAI and other generative models - Orchestrate multiple models and containers - Apply prompt engineering and fine-tuning |
| Implement knowledge mining and information extraction solutions | 15-20% | - Build knowledge bases and search indexes - Ingest and process structured/unstructured data - Extract entities, relationships, and key phrases - Implement intelligent search and retrieval |
| Implement an agentic solution | 5-10% | - Build agents with Microsoft Foundry Agent Service - Test, deploy, and optimize agents - Understand agent use cases and types - Develop multi-agent workflows and orchestration |
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309. Frage
You Build a bot in JavaScript.
From the Azure Command-Line interface (CLI), you run the following command.
az bot prepare-deploy
You need to deploy the bot to Azure.
Which three Azure CLl commands should you run in sequence? To answer, move the appropriate commands from the list of commands to the answer area and arrange them in the client order.
Antwort:
Begründung:
Explanation
To deploy the bot to Azure, you should run the following three Azure CLI commands in sequence:
az deployment group create This command will create the Azure resources for your bot using an ARM template and a parameters file. You need to specify the resource group name, the template file path, and the parameters file path. For example:
az deployment group create --resource-group myResourceGroup --template-file
"deploymentTemplates\template-with-preexisting-rg.json" --parameters
"deploymentTemplates\parameters-for-template-BotApp-with-rg.json"
This command will also output the app ID and password of your bot, which you will need for the next command1.
az webapp deployment source config-zip This command will deploy your bot code to the app service that you created in the previous step. You need to specify the resource group name, the app service name, and the zip file path of your bot code. For example:
az webapp deployment source config-zip --resource-group myResourceGroup --name myBotAppService --src
"code.zip"
This command will also output the URL of your bot endpoint, which you will need for the next command2.
az ad app update This command will update your bot registration with the endpoint URL of your bot.
You need to specify the app ID of your bot and the endpoint URL. For example:
az ad app update --id myBotAppId --set
replyUrls="https://myBotAppService.azurewebsites.net/api/messages"
This command will complete the deployment process and make your bot ready to be tested3.
310. Frage
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.
Antwort:
Begründung:
Explanation:
Box 1: LargeFaceListID
LargeFaceList: Add a face to a specified large face list, up to 1,000,000 faces.
Note: Given query face's faceId, to search the similar-looking faces from a faceId array, a face list or a large face list. A "faceListId" is created by FaceList - Create containing persistedFaceIds that will not expire. And a "largeFaceListId" is created by LargeFaceList - Create containing persistedFaceIds that will also not expire.
Incorrect Answers:
Not "faceListId": Add a face to a specified face list, up to 1,000 faces.
Box 2: matchFace
Find similar has two working modes, "matchPerson" and "matchFace". "matchPerson" is the default mode that it tries to find faces of the same person as possible by using internal same-person thresholds. It is useful to find a known person's other photos. Note that an empty list will be returned if no faces pass the internal thresholds. "matchFace" mode ignores same-person thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used in the cases like searching celebrity-looking faces.
Reference:
https://docs.microsoft.com/en-us/rest/api/faceapi/face/findsimilar
311. Frage
You are developing a text processing solution.
You develop the following method.
You call the method by using the following code.
GetKeyPhrases(textAnalyticsClient, "the cat sat on the mat");
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
static void GetKeyPhrases(TextAnalyticsClient textAnalyticsClient, string text)
{
var response = textAnalyticsClient.ExtractKeyPhrases(text);
Console.WriteLine("Key phrases:");
foreach (string keyphrase in response.Value)
{
Console.WriteLine($"\t{keyphrase}");
}
}
Then called with:
GetKeyPhrases(textAnalyticsClient, "the cat sat on the mat");
* "The call will output key phrases from the input string to the console."
* Yes.
* The method calls ExtractKeyPhrases, iterates over response.Value, and writes each key phrase to the console.
* "The output will contain the following words: the, cat, sat, on, and mat."
* No.
* The Text Analytics key phrase extraction returns meaningful phrases, not stop words or every word.
* Likely result: "cat", "mat", maybe "sat" depending on language model. But not "the", "on".
* "The output will contain the confidence level for key phrases."
* No.
* The ExtractKeyPhrases method in Text Analytics API returns only key phrases as strings, not confidence scores.
* The call will output key phrases from the input string to the console # Yes
* The output will contain the following words: the, cat, sat, on, and mat # No
* The output will contain the confidence level for key phrases # No
* Azure Text Analytics - Key Phrase Extraction
* TextAnalyticsClient.ExtractKeyPhrases
312. Frage
You have an Azure subscription that contains an Azure OpenA1 resource named All.
You plan to develop a console app that will answer user questions.
You need to call All and output the results to the console.
How should you complete the code? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
You are using the Azure OpenAI .NET SDK to send a prompt ("What is Microsoft Azure?") to a text model deployment. For text generation, you must call the Completions endpoint; hence client.GetCompletions(...) (or the async variant) is correct.
The Response < Completions > object returns one or more choices; the generated text is contained in response.Value.Choices[0].Text , which you can write to the console.
GetEmbeddings would return vector embeddings, not natural-language text, and GetImageGenerations is for DALL E image generation, so neither meets the requirement to "answer user questions" and print text.
Microsoft References
* Azure OpenAI Service (.NET) - Completions quickstart and sample showing GetCompletions and reading Choices[0].Text .
* Azure OpenAI Service - Embeddings overview (returns vectors, not text).
* Azure OpenAI Service - Image generation overview (DALL E, not for text answers).
313. Frage
You have a local folder that contains the files shown in the following table.
You need to analyze the files by using Azure Al Video Indexer.
Which files can you upload to the Video Indexer website?
Antwort: C
314. Frage
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