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NEW QUESTION # 91
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: Evaluators in Microsoft Foundry replace the need for configuring token limits. = No Evaluators are used to assess model or agent output quality, safety, and reliability. They do not replace model configuration settings such as token limits. Microsoft describes evaluators as tools that measure the quality, safety, and reliability of AI responses.
Statement 2: Evaluators in Microsoft Foundry can assess the quality and safety of responses generated by a generative AI model. = Yes This is correct. Microsoft Foundry includes built-in evaluators for general quality metrics such as coherence and fluency, safety/security metrics, RAG metrics, and agent-specific metrics.
Statement 3: Evaluators in Microsoft Foundry can retrain a deployed generative AI model automatically when quality issues are detected. = No Evaluators measure and report quality, safety, and reliability issues. They do not automatically retrain deployed generative AI models. Microsoft describes evaluation as measuring model or agent performance against test data, while monitoring can alert when outputs fail quality thresholds.
NEW QUESTION # 92
You deploy an Al system to assist with hiring decisions.
Company policy requires that human reviewers oversee Al-generated recommendations and remain responsible for final hiring decisions.
Which Microsoft responsible Al principle is this an example of?
Answer: B
Explanation:
Requiring human reviewers to oversee AI-generated hiring recommendations and remain responsible for the final hiring decisions is an example of the Microsoft responsible AI principle of accountability .
Accountability means people and organizations remain responsible for AI systems, their decisions, and their effects. In this scenario, the AI system assists with recommendations, but humans retain oversight and responsibility for the final decision.
NEW QUESTION # 93
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: In the new Microsoft Foundry portal, you must fine-tune a model before you can deploy the model. = No Fine-tuning is optional. Microsoft's Foundry model deployment documentation describes deploying Foundry Models directly from the model catalog for inference. It does not require fine-tuning first.
Statement 2: In the new Microsoft Foundry portal, you can test a model from the model catalog only after you deploy the model. = No Microsoft documentation states that some Foundry Tools are available to try via the model catalog without a project , and Foundry playgrounds are used for prototyping and validation before production.
Therefore, the statement using "only after you deploy" is too restrictive.
Statement 3: In the new Microsoft Foundry portal, you can deploy a model from the model catalog only after retraining the model. = No Retraining/fine-tuning is not required before deployment. Microsoft states that after you deploy a Foundry Model, you can interact with it in the Foundry Playground and use it from code, and the deployment workflow starts by selecting a model from the model catalog and choosing Deploy .
NEW QUESTION # 94
You are developing an application that will coordinate a travel-booking workflow by gathering user preferences, searching for options, requesting approval, and booking services. The solution will maintain context across the steps.
Which type of Al workload is this an example of?
Answer: A
Explanation:
The described solution coordinates a multi-step workflow: gathering user preferences, searching for options, requesting approval, booking services, and maintaining context across steps. This is an example of Agentic AI.
Agentic AI systems use AI agents to reason over user goals, maintain context, decide which actions to take, and use tools or services to complete tasks across multiple steps.
Why the other options are incorrect:
A). information extraction extracts structured data from content.
C). computer vision analyzes images or video.
D). generative AI creates new content, but the scenario specifically describes an autonomous, context-aware workflow with actions, which is Agentic AI.
NEW QUESTION # 95
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
When content is submitted to Azure Content Understanding in Foundry Tools, the analysis is asynchronous.
This means the service does not return results immediately within the same HTTP request. Instead, it uses the standard Azure long-running operation (LRO) pattern - you call begin_analyze() to submit the content, which immediately returns a poller object, and then call poller.result() to wait for processing to complete and retrieve the structured extraction results.
Why the other options are wrong:
Synchronous is incorrect - the analysis pipeline involves multiple AI steps (OCR, speech transcription, schema mapping) that take time; a blocking synchronous call is not supported.
Returned only as unstructured plain text is incorrect - Azure Content Understanding returns richly structured JSON output with named fields mapped to your defined schema, not plain unstructured text.
Limited to OCR-only processing is incorrect - Content Understanding goes far beyond OCR; it supports document, audio, image, and video analyzers, and performs semantic field extraction using AI, not just character recognition.
This asynchronous design is consistent across all Azure AI services that perform complex, multi-step content processing.
NEW QUESTION # 96
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