HOTSPOT (Drag and Drop is not supported)Select the answer that correctly completes the sentence.Hot Area:
Answer(s): A
Box: accountability Ensuring that human reviewers oversee AI-generated decisions and remain responsible for the final outout is an example if the Microsoft responsible AI princioke of ___________.The principle of accountability states that the people who design, deploy, and use AI systems must remain responsible and answerable for how those systems operate and the outcomes they produce. Under Microsoft's Responsible AI Principles, accountability is defined by the following key ideas: Human Oversight: AI should not be the sole decision-maker for critical matters; human reviewers must have the ability to supervise, control, and override AI-generated outputs. Ultimate Responsibility: Even when AI automates tasks or processes, humans and organizations remain legally and ethically responsible for the final decision.
https://thedatacommunity.org/2026/05/18/describe-considerations-for-accountability-in-an-ai-solution-ai-901-exam-prep/
You need to build an AI solution that generates marketing email drafts based on a short description of a product and its target audience.Which AI workload should you use?
Answer(s): C
A Generative AI workload is exactly what should be used for this scenario. Generative AI models (such as LLMs) are specifically designed to interpret prompts and create original, context-aware content, making them the perfect fit for drafting tailored marketing emails based on a product description and target audience.
https://azure.microsoft.com/en-us/products/ai-foundry/tools/content-understanding
HOTSPOT (Drag and Drop is not supported)For each of the following statements, select Yes if the statement is true. Otherwise, select No.Note: Each correct selection is worth one point.Hot Area:
Box 1: Yes Yes - The temperature parameter can be set before deploying a model.The temperature parameter can be set before deploying a model. Its actual enforcement depends on where and how the model is being deployed.Box 2: No No - During inference, the model name us used to route requests to a specific deployment.During inference, the deployment name is used to route requests to a specific deployment, not the model name.When you deploy a foundational model in Azure AI (such as Azure OpenAI or Azure AI Foundry models), you assign it a custom unique deployment name. During inference, you must pass this deployment name into the model parameter of your SDK call or request body.Box 3: Yes Yes - After a model is deployed, the model deployed, both code and testing tools can be used to interact with the model?After you deploy a model in Azure AI (such as in Azure AI Foundry or Azure Machine Learning), you can interact with it using both custom code and native testing tools.
https://github.com/shivamag125/EM_PT https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/endpoints
Box: fairness Evaluating model outcomes across demographic groups to reduce bias is an example of the Microsoft responsible AI principle of ________.Evaluating model outcomes across demographic groups to reduce bias falls squarely under Microsoft's responsible AI principle of Fairness.According to Microsoft's AI framework, AI systems should treat all people fairly, which requires teams to analyze how a system's predictions and recommendations impact different groups of people and actively mitigate issues like stereotyping, bias, and unfairness.
Box: be explainable to users The Microsoft responsible AI principle of transparency requires that AI systems be _________.Explainability is a core component of transparency in Microsoft's Responsible AI principles. The framework requires that AI systems be understandable to users, meaning people should comprehend how decisions are made, what data is used, and recognize when they are interacting with AI.
https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai
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