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NEW QUESTION # 104
Verifying that machine learning models do NOT show racial or gender bias is an example of which Microsoft responsible AI principle?
Answer: C
Explanation:
Fairness and inclusiveness in Azure Machine Learning: The fairness assessment component of the Responsible AI dashboard enables data scientists and developers to assess model fairness across sensitive groups defined in terms of gender, ethnicity, age, and other characteristics.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2
NEW QUESTION # 105
Which Azure OpenAI model can be used to develop code?
Answer: C
Explanation:
The GPT-4 32k model can be used to develop programming code. It has a large context window, allowing it to process more information and generate more complex code. The model has shown strong performance in coding tasks, even surpassing its predecessor, o3-mini, in some domains.
Reference:
https://vivekupadhyay1.medium.com/openai-chat-gpt-4-32k-detailed-document-ce3486e4786e
NEW QUESTION # 106
You are designing a system that will generate insurance quotes automatically.
Match the Microsoft responsible AI principles to the appropriate requirements.
To answer, drag the appropriate principle from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
Requirement
Principle
The decision-making process must be recorded so that staff can identify the reasoning behind a particular quote.
Transparency
A customer ' s personal information must be visible only to staff who are involved in the decision-making process.
Privacy and security
The system must be accessible to customers who use screen readers or other assistive technology.
Inclusiveness
Microsoft ' s Responsible AI framework defines six core principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability .
The first requirement maps to Transparency because personnel must be able to understand and review how the AI system arrived at an insurance quote. Transparency concerns making AI behavior, capabilities, limitations, and decision processes understandable. Recording the decision-making process provides the traceability required to explain a particular result.
The second requirement maps to Privacy and security . Restricting customer information to authorized staff directly implements least-privilege access and protects confidential personal data from unauthorized disclosure. Microsoft specifically associates this principle with respecting data boundaries and safeguarding information handled by AI systems.
The third requirement maps to Inclusiveness . AI systems should accommodate the full range of people they serve, including individuals who rely on assistive technologies. Support for screen readers and accessibility technologies ensures that users with disabilities are not excluded from interacting with the system. Microsoft explicitly connects inclusive AI design with accessibility considerations for people with disabilities.
NEW QUESTION # 107
You need to create an AI agent in Microsoft Foundry that follows a specific role and behavior when responding to users. What should you configure?
Answer: A
Explanation:
In Microsoft Foundry, instructions (formerly referred to as system messages) are the primary way to define the role, behavior, tone, and constraints of your AI agent.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/agents/quickstart
NEW QUESTION # 108
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 # 109
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