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問題 #28
Select the answer that correctly completes the sentence.
答案：
解題說明：
Explanation:
The Microsoft responsible AI principle of transparency requires that AI systems be explainable to users
.
Transparency means users should understand when they are interacting with AI, what the system can and cannot do, and how outputs or predictions are generated at an appropriate level.
The other options map to different responsible AI principles:
protect sensitive user data = Privacy and security
reduce bias in decisions = Fairness
require human oversight = Accountability
問題 #29
Which type of compute resource should you use to attach an existing Azure Kubernetes Service (AKS) cluster to Azure Machine Learning?
答案：C
解題說明：
To attach an existing Azure Kubernetes Service (AKS) cluster to Azure Machine Learning, you should use the "Attached Compute" or "Kubernetes Compute Target". This allows you to leverage your existing AKS cluster as a compute resource for your machine learning tasks within Azure Machine Learning. You can achieve this using the Azure CLI v2, Python SDK v2, or Machine Learning Studio UI.
1. Kubernetes Compute Target:
Azure Machine Learning treats your AKS cluster as a compute target, allowing you to specify it as the location for running your training jobs or deploying models.
2. Attached Compute:
This refers to the ability to connect existing compute resources, like your AKS cluster, to your Azure Machine Learning workspace.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-to-workspace
問題 #30
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
Statement 1: An Azure Content Understanding in Foundry Tools analyzer returns results in the HTML format. = No Azure Content Understanding returns analysis results as structured output, typically JSON , not HTML.
Microsoft documentation states that the Content Understanding API returns analysis results in a structured JSON format.
Statement 2: The Content Understanding REST API analyzes content synchronously and returns results in the same response. = No Content Understanding analysis is asynchronous. The quickstart states that the response header includes an Operation-Location field, which is used to retrieve the results of the asynchronous analysis operation.
Statement 3: Azure Content Understanding in Foundry Tools can transform documents, images, audio, and video files into structured output. = Yes Microsoft describes Azure AI Content Understanding as processing documents, images, videos, and audio, and transforming them into user-defined output formats.
問題 #31
You are developing an application that records a user's voice and sends the recorded audio to a deployed multimodal model in Microsoft Foundry.
You need to send the user's request to the model.
What should you include in the request?
答案：D
解題說明：
An audio input prompt must be included in the request when sending recorded voice directly to a native multimodal audio/speech model deployed in Microsoft Foundry.
Audio input prompt: Native multimodal models (such as audio-enabled GPT series deployed via Azure OpenAI in Microsoft Foundry Models) accept raw or base64-encoded audio directly as an input content type/modality in the inference payload.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/audio-completions-quickstart
問題 #32
You are developing an application that analyzes invoices by using Azure Content Understanding in Foundry Tools.
You have a custom analyzer named invoiceAnalyzer.
You need to use the analyzer to process invoice files.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
The correct parameter is analyzer_id . The Azure AI Content Understanding Python SDK uses the ContentUnderstandingClient.begin_analyze() method to submit content for processing. Microsoft defines the method signature with analyzer_id as the required identifier that specifies which analyzer should process the supplied input.
Therefore, the code should be completed as:
poller = client.begin_analyze(analyzer_id= " invoiceAnalyzer " , ...)
A custom analyzer encapsulates the configuration that determines how Content Understanding interprets the source content and which fields or structured information it extracts. Because the analyzer has already been created with the name invoiceAnalyzer , that value is supplied through analyzer_id when the analysis operation begins.
extraction_type is not the parameter used to select an analyzer. model_name would refer conceptually to an underlying AI model, but Content Understanding analysis is invoked through an analyzer abstraction rather than by specifying a model directly. Likewise, schema_id does not identify the analyzer to execute.
Microsoft ' s custom-analyzer guidance explicitly demonstrates client.begin_analyze (analyzer_id=analyzer_id, inputs=[...]) , confirming the required SDK pattern
問題 #33
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