AI-901 Exam Dumps - Microsoft Certified: Azure AI Fundamentals Questions and Answers

The completed sentence is:

An AI workload that produces new content based on user input is an example of generative AI .

Generative AI is used to create new content, such as text, images, code, audio, or other outputs, from user prompts or instructions.

The other options are incorrect:

content understanding extracts or interprets information from existing content.

information extraction identifies and extracts structured data from content.

text analysis analyzes existing text for entities, sentiment, key phrases, language, or summaries.

input=[

{

"role": "user",

"content": [

{"type": "input_text", "text": "What is in this image? Provide 3 bullet points."},

{"type": "input_image", "image_url": image_url},

],

}

]

print(response.output_text)

For a multimodal request that sends both text and an image to an Azure OpenAI model, the input content array uses separate content item types. Text content is sent with type: "input_text" , and image content is sent with type: "input_image" . Microsoft’s Azure OpenAI Responses API documentation shows the Responses API supports multimodal inputs and uses structured input content items in the request.

The options output_text and output_image are not used inside the user input content array. In the shown code, response.output_text is used after the response is returned to print the model’s text answer.

Statement 1: An AI generative model is retrained before performing each user request. = No

A generative AI model is not retrained for every user request. During normal use, the model performs inference: it receives input, processes the prompt, and generates a response based on the deployed model.

Statement 2: An AI agent responds by copying and pasting answers stored in a database. = No

An AI agent does not simply copy and paste stored answers. Agents use generative AI models, instructions, context, and optionally tools or connected data sources to reason over user input and produce responses or actions.

Statement 3: An AI agent uses a generative AI model to establish actions based on user input. = Yes

This is correct. An AI agent uses a generative AI model together with instructions and available tools to interpret user input, determine the next action, and generate a response.

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.

AudioOutputConfig(filename="output.wav")

The question specifically states the application must save the synthesized audio to a file . In the Azure Speech SDK for Python, speechsdk.audio.AudioOutputConfig(filename="output.wav") directs the synthesizer to write the generated speech output directly to a WAV file on disk — which is exactly the requirement.

Why the other options are wrong:

AudioOutputConfig(stream) — This routes audio output to an in-memory audio stream object, not a file. It is used when you want to process or play the audio programmatically without saving it to disk.

AudioStreamFormat(wave_stream_format=AudioStreamWaveFormat.PCM) — This class defines the format of an audio stream (e.g., PCM encoding, sample rate). It is used when configuring custom audio streams, not when specifying a file output destination. It is not a valid argument for speechsdk.audio. in this context.

The correct and complete line is:

audio_config = speechsdk.audio.AudioOutputConfig(filename="output.wav")

Microsoft’s Foundry Agent Service documentation states that tool_choice provides deterministic control over tool calling:

auto means the model decides whether to call tools.

required means the model must call one or more tools .

none means the model does not call tools.

Therefore:

A. auto = Incorrect, because the model may or may not call the Azure function.

B. none = Incorrect, because this prevents tool/function calls.

C. required = Correct, because it forces the agent to call a tool.

The Azure OpenAI function-calling documentation also confirms that tool_choice="auto" lets the model decide whether to call a function, while tool_choice="none" forces a user-facing response without a tool call.

client = OpenAI(

api_key="...",

base_url="https://resource1.openai.azure.com/openai/v1/",

)

response = client.responses.create(

model="my-mini-gpt",

)

For Azure OpenAI in Microsoft Foundry, the base_url uses the Azure OpenAI resource name in the endpoint format:

https:// < resource-name > .openai.azure.com/openai/v1/

In the question, the Azure OpenAI resource is named Resource1 , so the first blank must be resource1 . Microsoft documentation for Azure OpenAI v1 endpoints confirms that the endpoint must use the ...openai.azure.com/openai/v1/ path.

For the model parameter, Azure OpenAI requires the deployment name , not the underlying model name. Microsoft states that Azure OpenAI always requires the deployment name when calling APIs, even when the parameter is named model.

The deployed model is gpt-4.1-mini , but the deployment name is my-mini-gpt . Therefore, the second blank must be:

model="my-mini-gpt"

So the correct selections are:

base_url blank = resource1

model blank = my-mini-gpt

Statement 1: Generating a response to a user prompt occurs during the inference stage. = Yes

When a deployed generative AI model receives a user prompt and generates an output, that process is called inference . The model is being used to produce a response, not being trained.

Statement 2: A generative AI model generates responses by copying stored documents directly from the model’s training data. = No

Generative AI models do not normally respond by simply copying stored documents from training data. They generate outputs based on learned statistical patterns. In some cases, models may reproduce memorized content, but that is not the normal or intended generation mechanism.

Statement 3: A generative AI model produces output by predicting the next token based on patterns learned from the model’s training data. = Yes

This is correct. Large language models generate text by predicting likely next tokens based on the prompt and learned patterns from training.

To minimize cost and keep generated responses within a defined length, configure Max Completion Tokens .

Microsoft’s Azure OpenAI / Foundry API reference defines max_completion_tokens as an upper bound for the number of tokens that can be generated for a completion . Because generated tokens contribute to usage and response length, limiting completion tokens helps control both output length and cost.

Temperature and Top P control randomness or sampling behavior, not maximum response length. Model version settings do not directly define the generated response length.