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Microsoft AI-901 Exam Syllabus Topics:
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Reference: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-901
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Microsoft Azure AI Fundamentals Sample Questions:
You have a Microsoft Foundry project that contains a generative AI model deployment.
You test the model by using the Foundry playground.
You need to develop an application that sends requests to the deployed model.
Which information must the application include to call the model?
- A. The Foundry project display name
- B. The model training dataset
- C. The model endpoint and authentication credentials
- D. The exported playground session history
Correct Answer: C 🗳️
Explanation: Only visible for VCEDumps members. You can sign-up / login (it's free).
Select the answer that correctly completes the sentence.
Correct Answer:
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.
You need to compare the costs of large language models (LLMs) for a generative AI solution.
What should you use in the Microsoft Foundry portal?
- A. Evaluator catalog
- B. Compliance
- C. Tools
- D. Model leaderboard
Correct Answer: D 🗳️
Explanation: Only visible for VCEDumps members. You can sign-up / login (it's free).
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:
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.
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
Ongoing monitoring, feedback, and evaluation are goals of the Reliability & Safety principle.
The Microsoft responsible AI principle of Reliability and safety focuses on ensuring that AI systems operate consistently, safely, and as intended across different conditions. This includes testing, monitoring, collecting feedback, and evaluating system performance over time.
Why the other options are incorrect:
Human oversight and control aligns more closely with accountability.
Data governance and management aligns more closely with privacy and security.
The minimization of stereotyping, demeaning, and erasing outputs aligns more closely with fairness and inclusiveness.
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