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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe fundamental principles of machine learning on Azure | 25-30% | - Azure Machine Learning basics
|
| Topic 2: Describe Artificial Intelligence workloads and considerations | 20-25% | - Responsible AI principles
|
| Topic 3: Describe features of natural language processing (NLP) workloads on Azure | 15-20% | - Text analysis
|
| Topic 4: Describe features of generative AI workloads on Azure | 10-15% | - Generative AI concepts
|
| Topic 5: Describe features of computer vision workloads on Azure | 15-20% | - Azure AI Vision services
|
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NEW QUESTION # 63
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is endpoint of the model . After a vision-enabled GPT model is deployed in Microsoft Foundry, the deployment exposes an inference endpoint that applications can call programmatically. The application sends requests containing text and, when required, image content to the deployed model through the appropriate chat-completions API endpoint. Microsoft's documentation explicitly states that to use vision- enabled models, you call the Chat Completion API against a supported model that has been deployed.
The Foundry playground is primarily an interactive environment for testing prompts, model behavior, and multimodal inputs during development. It is not the production interface that an external application uses to submit inference requests. An evaluation pipeline is used to assess model or application quality against defined metrics and datasets, while a training dataset is associated with model training or fine-tuning rather than serving inference traffic.
Microsoft Foundry provides the endpoint and required authentication information after deployment.
Applications then invoke that endpoint using an SDK or REST API, passing text and image inputs as part of the request payload.
NEW QUESTION # 64
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Statement 1: Fairness can be achieved by focusing solely on improving the overall accuracy of an AI model. = No Overall accuracy alone does not guarantee fairness. A model can have high overall accuracy while still producing biased or lower-quality outcomes for specific demographic groups. Fairness requires evaluating and reducing differences in model behavior across groups.
Statement 2: Evaluating AI system outputs to identify and reduce bias across demographic groups supports fairness. = Yes This statement is correct. The Microsoft responsible AI principle of fairness means AI systems should treat people fairly and avoid unfair bias. Evaluating outputs across demographic groups helps identify and reduce bias.
Statement 3: Ensuring fairness for an AI system means that all users always receive the same output from the system. = No Fairness does not mean identical outputs for all users. It means the system should avoid unfair bias and provide equitable treatment. Different users may receive different outputs when differences are relevant and appropriate.
NEW QUESTION # 65
You are explaining generative AI workflows to a colleague. What is the first step of a generative AI workflow?
Answer: B
NEW QUESTION # 66
You are developing an application that extracts fields from PDFs by using Azure Content Understanding in Foundry Tools.
You need to use the Python SDK to submit a PDF for analysis and retrieve the extraction results.
What should you do?
Answer: D
Explanation:
Azure Content Understanding analysis operations are long-running operations in the Python SDK. Microsoft's Python SDK documentation states that analysis operations return a poller, and the SDK provides LROPoller types that handle polling automatically when you call .result().
Therefore, the correct workflow is to submit the PDF by calling begin_analyze(), receive a poller, and then call:
result = poller.result()
Option B is incorrect because extraction results are not read from request headers.
Option C is incorrect because the requirement is to use Azure Content Understanding extraction, not build a manual OCR-only mapping pipeline.
Option D is incorrect because the SDK analysis pattern is asynchronous/long-running, not a simple synchronous analyze() call that returns all extracted fields in the same request.
NEW QUESTION # 67
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Evaluating model outcomes across demographic groups to reduce bias is an example of the Microsoft responsible AI principle of fairness.
The Microsoft responsible AI principle of fairness means AI systems should treat all people fairly and avoid unfair bias or discrimination across groups.
The other options are incorrect:
accountability means people and organizations remain responsible for AI systems and their effects.
privacy and security focuses on protecting data and controlling access.
transparency means users should understand when and how AI is being used, including system capabilities and limitations.
NEW QUESTION # 68
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