정답:

Explanation:

The completed sentence is:
When developing an AI system for self-driving cars, the Microsoft reliability and safety principle for responsible AI should be applied to ensure consistent operation of the system during use.
Self-driving cars are safety-critical AI systems. The key requirement in the question is consistent operation of the system during use , which maps directly to the Microsoft responsible AI principle of reliability and safety .
The other options are incorrect:
Fairness focuses on reducing bias and treating people equitably.
Accountability focuses on ensuring people remain responsible for AI systems and their outcomes.
Inclusiveness focuses on designing AI systems that work for and empower diverse users, including people with different needs or abilities.
Therefore, the correct answer is reliability and safety .

정답:

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.

정답: C  🗳️

설명: (Fast2test 회원만 볼 수 있음)

정답:

Explanation:
Statement 1: The Temperature parameter can be set before deploying a model. = No temperature is an inference/request parameter used when calling or testing a deployed model. It controls randomness in generated responses. It is not a required setting for deploying the model itself.
Statement 2: During inference, the model name is used to route requests to a specific deployment. = No In Azure OpenAI / Microsoft Foundry deployments, application requests are routed to a specific deployment name , even when the SDK parameter is called model. The underlying model name, such as gpt-4.1-mini, is not what routes the request to the deployment.
Statement 3: After a model is deployed, both code and testing tools can be used to interact with the model. = Yes After deployment, you can test the model in Foundry playground/testing tools or call the deployment from application code by using the endpoint, deployment name, and authentication credentials.