Passed Exam Today AI-901

Statement 1: Human-in-the-loop practices provide accountability for AI-generated decisions. = Yes

Microsoft responsible AI guidance recommends keeping a human in the loop, maintaining human oversight, and ensuring humans have a role in decisions based on model output.

Statement 2: Deploying an AI system to a production environment eliminates the need for ongoing monitoring. = No

Microsoft guidance states that after deployment, an AI-powered product or feature requires ongoing monitoring and improvement.

Statement 3: Disclosing the team that designed and deployed an AI system provides accountability for the system’s output. = Yes

Accountability in responsible AI means people and organizations remain responsible for AI systems and their effects. Identifying the people or team responsible for designing and deploying the system supports accountability and governance.

The REST API request contains an image-generation style payload:

{

" prompt " : " A polar bear on the beach holding an icecream " ,

" size " : " 1024x1024 " ,

" n " : 1,

" style " : " vivid "

}

This request asks the model to generate an image from a text prompt. In Azure OpenAI, DALL-E models are used for image generation from natural language prompts.

Why the other options are incorrect:

B. Codex is associated with code generation tasks.

C. GPT-4.1 is primarily used for text/chat and multimodal reasoning, not this classic image-generation API payload.

D. Whisper is used for speech-to-text transcription.

The requirement is to convert written customer notifications into natural-sounding spoken audio. This is speech synthesis, also known as text to speech.

Microsoft’s Azure Speech documentation describes text to speech as a capability that converts text into natural-sounding synthesized speech. Therefore, for playing written notifications over a phone system, the correct Azure Speech capability is speech synthesis.

Why the other options are incorrect:

A. speaker recognition identifies or verifies speakers by voice.

C. speech recognition converts spoken audio into text.

D. speech translation translates spoken audio between languages.

The correct answer is API endpoint. After a generative AI model is deployed in Microsoft Foundry, the deployment exposes an inference endpoint through which external applications can submit requests and receive generated responses. Microsoft Foundry documentation describes deployed models as being accessible programmatically through an endpoint and associated credentials. Applications invoke the relevant REST API or SDK against this endpoint and specify the deployed model as required by the API.

An embedding vector is a numerical representation of semantic information used for similarity search, retrieval, and grounding; it is not the network interface through which a deployed model is invoked. A training dataset contains data used to train or fine-tune a model and is not called when performing inference. A URL parameter can be part of an API request, but it does not represent the deployed model ' s callable interface.

This directly aligns with the AI-901 objective to deploy a model and interact with it in the Foundry portal and to create lightweight applications that invoke deployed generative AI models through supported APIs and SDKs.