AI-901 Microsoft Azure AI Fundamentals (Updated Version) Questions and Answers

Ongoing monitoring, feedback, and evaluation are goals of the Reliability & Safety principle.

The Microsoft responsible AI principle of Reliability and safety focuses on ensuring that AI systems operate consistently, safely, and as intended across different conditions. This includes testing, monitoring, collecting feedback, and evaluating system performance over time.

Why the other options are incorrect:

Human oversight and control aligns more closely with accountability.

Data governance and management aligns more closely with privacy and security.

The minimization of stereotyping, demeaning, and erasing outputs aligns more closely with fairness and inclusiveness.

The correct answer is natural language processing. In this scenario, a user communicates with a chatbot using ordinary human language, such as asking, “Will it rain today?” The chatbot must interpret the meaning and intent of the user ' s text, process the request, and formulate a natural-language response containing the relevant weather information. These are characteristic natural language processing (NLP) functions.

The current AI-901 study guide explicitly includes text analysis among the common AI workloads candidates must identify, and it also covers techniques for processing and interpreting human language. Microsoft documentation on conversational language understanding explains that conversational systems interpret user utterances to determine the user ' s intent and extract relevant information before the application performs an appropriate action.

Although the weather information itself may originate from a forecasting system, the scenario being tested centers on the chatbot interaction—understanding a natural-language question and responding conversationally. Therefore, prediction and forecasting is not the best classification of the complete interaction. Computer vision is also incorrect because no images or visual information are being analyzed.

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.

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

A schema defines which fields to extract when analyzing content.

In Azure Content Understanding, the schema or fieldSchema defines the structured data that the analyzer extracts from content, including field names, types, and extraction behavior. Microsoft documentation states that Content Understanding lets you define a schema to extract, classify, or generate field values from unstructured content.

The other options are incorrect:

A keyword list does not define the complete structured output fields.

OCR-only processing extracts text, but it does not define structured fields.

A synchronous API call describes a request pattern, not the extraction schema.

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.

To send chat prompts to a deployed model, the application needs authentication credentials, the endpoint URL, and the model deployment name. Microsoft’s Foundry SDK guidance shows authentication with credentials, an endpoint, and the model= " model_deployment_name " value when calling a deployed model.

To initialize the Azure Speech SDK for Python, you create a SpeechConfig object with information for the Speech resource. Microsoft documentation states that SpeechConfig includes information such as the Speech resource key and associated region, endpoint, host, or authorization token. It also shows the Python pattern:

speech_config = speechsdk.SpeechConfig(subscription= " YourSpeechKey " , region= " YourSpeechRegion " )

or, for endpoint-based configuration:

speech_config = speechsdk.SpeechConfig(subscription=os.environ.get( " SPEECH_KEY " ), endpoint=os.environ.get( " ENDPOINT " ))

Therefore, among the provided options, the correct answer is D. the Foundry resource key and endpoint.

Options A, B, and C are incorrect because Speech SDK initialization does not use an OpenAI model deployment name, storage account credentials, or the Foundry project name/project endpoint.

The completed sentence is:

To define an agent ' s role and behaviors, you must configure a system prompt for the agent.

In Microsoft Foundry / Azure AI agent scenarios, the system prompt, also referred to as system instructions, defines how the agent should behave, what role it should follow, and what constraints it must observe.

The other options are incorrect:

deployment slot relates to hosting/deployment routing, not agent behavior.

embedding index is used for vector search or retrieval scenarios, not defining the agent’s role.

fine tuning job changes or adapts model behavior through training, but it is not the standard configuration used to define an agent’s role and behavior at runtime.

Therefore, the correct answer is system prompt.

Azure Content Understanding analysis operations are asynchronous long-running operations. Microsoft’s Python SDK documentation states that analysis starts with a begin operation, then the application polls for results until analysis completes.

Therefore, after submitting the PDF for analysis, the application should poll the operation results until the asynchronous analysis job succeeds.

Statement 1: To process prompts that contain images, you must deploy a multimodal generative AI model. = Yes

Image prompts require a vision-enabled or multimodal model. A text-only model cannot interpret image content.

Statement 2: In the Foundry playground, you can provide an image by uploading a local file or by specifying a publicly accessible URL. = Yes

The Foundry playground for vision-capable models supports adding image input to a prompt, including local uploads and image URLs.

Statement 3: A multimodal model can interpret multiple images included in a single prompt. = Yes

Vision-enabled multimodal models can accept image content items in a prompt, and multiple images can be included for the model to analyze together.

Azure Content Understanding uses transcription to convert audio content, such as voicemail recordings, into text. Microsoft’s Azure Content Understanding audio documentation states that transcription converts conversational audio into searchable and analyzable text-based transcripts.

Option A. Voice Live is not the feature used by Content Understanding to convert voicemail recordings into text.

Option B. key phrase extraction identifies important phrases after text is available; it is not the audio-to-text conversion step.

Option D. optical character recognition (OCR) is for extracting text from images or documents, not audio.

Therefore, the correct answer is C. transcription.

Azure Text Analytics client library

The completed sentence is:

To develop an application that analyzes text by using Azure Language in Foundry Tools, use the Azure Text Analytics client library package.

Azure Language text analysis features include sentiment analysis, key phrase extraction, named entity recognition, language detection, and other text analytics capabilities. These are accessed by using the Azure Text Analytics client library.

The highlighted Azure Speech SDK option is incorrect because it is used for speech workloads, such as speech recognition and speech synthesis, not text analysis.

Speech

The correct answer is speech . The scenario specifically describes converting spoken customer requests into written text , which is a speech-to-text , or speech recognition, capability. Microsoft classifies speech as one of the common AI workloads in the current Azure AI Fundamentals study guide. The guide explicitly requires candidates to identify scenarios involving speech and to understand the capabilities of speech recognition and speech synthesis .

Microsoft Azure Speech in Foundry Tools provides speech-to-text functionality that converts audio streams into textual transcriptions. It supports real-time transcription, fast transcription, and batch transcription depending on the workload.

Key phrase extraction analyzes existing text to identify important words or concepts; it does not perform audio transcription. Text analysis covers operations such as sentiment analysis, entity recognition, summarization, and key phrase extraction after text already exists. Natural Language Understanding (NLU) interprets the meaning or intent of language, but the conversion from audio into written words itself is specifically a speech recognition workload .

Study Guide Reference: Identify AI concepts and capabilities → Identify AI workloads → Identify features and capabilities of speech recognition and speech synthesis.

Verifying that machine learning models do not show racial or gender bias is an example of the Microsoft responsible AI principle of fairness.

Fairness means AI systems should treat people equitably and avoid unfair bias across demographic groups such as race, gender, age, or other protected characteristics.

Why the other options are incorrect:

A. Safety focuses on avoiding harm and operating safely.

B. Privacy and security focuses on protecting data and access.

C. Reliability focuses on consistent and dependable operation.

D. Fairness directly addresses bias and equitable treatment.

The completed sentence is:

After deploying a vision-enabled GPT model in Microsoft Foundry, you can configure an application to send requests to the endpoint of the model.

To call a deployed model from an application, the application must send requests to the deployed model’s endpoint and include the required authentication credentials. The Foundry playground is used for testing interactively, but application code calls the deployed model endpoint.

The other options are incorrect:

evaluation pipeline of model is used to evaluate model performance, not to receive inference requests from an app.

Foundry playground is an interactive testing environment, not the production target for application requests.

training dataset of the model is used for training or fine-tuning, not for sending inference requests.

Therefore, the correct answer is endpoint of the model.

A recycling machine that uses cameras to identify bottles of the correct shape and reject other items requires analysis of visual input. This is a computer vision workload.

Computer vision is used for image and video analysis tasks such as object detection, classification, shape recognition, and visual inspection.

The other options are incorrect:

A. knowledge mining extracts insights from large collections of documents or data.

B. natural language processing analyzes or generates language.

C. generative AI creates new content, but the requirement is to inspect camera images and identify objects.

Azure Vision in Foundry Tools supports OCR capabilities for reading printed and handwritten text from images and documents. Microsoft documentation states that OCR extracts text from images and documents, including handwritten text.

Therefore, extracting data from handwritten letters is the action that can be performed by using Azure Vision in Foundry Tools.

Creating new illustrations based on the text of an article is a content generation task. Because the AI solution must create new visual content from a text prompt or written input, the correct workload is generative AI.

Statement

Answer

Defining an AI system for a typical user profile is sufficient to support inclusiveness.

No

A high number of active users means an AI system provides an inclusive experience.

No

Providing accessible interfaces and language options helps support inclusiveness in an AI system.

Yes

Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide/topics:

The correct sequence is No, No, Yes.

Microsoft defines inclusiveness as designing AI systems so they work effectively for the full range of people they are intended to serve. The current AI-901 study guide explicitly includes the ability to describe considerations for inclusiveness in an AI solution as part of the Responsible AI objective domain.

The first statement is No because designing only for a hypothetical or “typical” user can exclude people with different abilities, languages, interaction styles, cultural backgrounds, or accessibility requirements. Inclusive design deliberately considers diverse and potentially underserved users.

The second statement is No because the number of active users is an adoption or usage metric. A system may have millions of users while still creating accessibility barriers or excluding particular groups. Usage volume therefore does not demonstrate inclusiveness.

The third statement is Yes. Accessible interfaces, assistive-technology compatibility, and suitable language options reduce barriers and broaden the range of people who can effectively interact with an AI system. Microsoft specifically emphasizes accessibility and supporting varied user needs as core elements of inclusive AI design.

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.

For thousands of recorded support calls stored as audio files in Azure Storage, the correct capability is speech to text batch transcription.

Microsoft states that batch transcription is designed to transcribe a large amount of audio data in storage, including audio files in Azure Blob Storage, and that files can be processed concurrently to reduce turnaround time.

Real-time transcription is for live audio, not large stored batches. Text to speech converts text into audio. Speech translation translates speech between languages, but the requirement is to generate transcripts.

The correct Python method is:

recognizer.recognize_once()

Completed code:

import azure.cognitiveservices.speech as speechsdk

speech_config = speechsdk.SpeechConfig(subscription=key, region=region)

recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config)

recognizer.recognize_once()

Microsoft’s Azure Speech documentation for Foundry Tools explains that Speech to text is used for real-time speech recognition and converting spoken audio into text. The Python Speech SDK uses a SpeechRecognizer object for recognition from audio input, such as a microphone.

Why the other options are incorrect:

recognizer.speak_text_async( " Ready " ) is incorrect because speaking text is text-to-speech, not speech-to-text recognition.

recognizer.start_continuous_recognition() can be used for continuous recognition, but the code shown is asking for the basic method to recognize spoken input and convert it to text from the SpeechRecognizer.

recognizer.start_keyword_recognition() is used for keyword/wake-word recognition, not general speech-to-text transcription of spoken commands.

Therefore, the correct answer is:

recognizer.recognize_once()

The Model catalog is used for comparing and deploying a wide range of models for generative AI development.

Microsoft states that the model catalog in Foundry portal is the hub for discovering and using a wide range of models to build generative AI applications. Microsoft training also states that the model catalog is used to compare models by benchmark metrics and deploy models to endpoints.

Azure Content Understanding analysis operations are long-running operations in the Python SDK. Microsoft’s Python SDK documentation states that analysis operations return a poller, and the SDK provides LROPoller types that handle polling automatically when you call .result().

Therefore, the correct workflow is to submit the PDF by calling begin_analyze(), receive a poller, and then call:

result = poller.result()

Option B is incorrect because extraction results are not read from request headers.

Option C is incorrect because the requirement is to use Azure Content Understanding extraction, not build a manual OCR-only mapping pipeline.

Option D is incorrect because the SDK analysis pattern is asynchronous/long-running, not a simple synchronous analyze() call that returns all extracted fields in the same request.

To call a deployed Azure OpenAI model from an application, the app must know the service endpoint and authenticate its request. Microsoft documentation states that Azure OpenAI supports API key authentication or Microsoft Entra ID authentication, and API key authentication requires including the API key in the request. Microsoft quickstart guidance also states that to successfully make a call against Azure OpenAI, you need an endpoint and a key.

The application does not need the model training dataset, the Foundry project display name, or exported playground session history to call the deployed model.

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.

For a vision-enabled GPT model deployment, use the Azure OpenAI Responses API to send text and image inputs in a single request. Microsoft’s Foundry SDK documentation states that the endpoint exposes the Responses API and provides access to Azure OpenAI models, including chat completions and multimodal-capable model interactions.

The Microsoft responsible AI principle of transparency means AI systems should be understandable. Users should know when they are interacting with AI and should be able to understand the system’s purpose, capabilities, limitations, and the basis for its decisions.

Therefore, helping users understand the decisions made by an AI system is an example of transparency.

images.generate

The canonical Python call is:

response = client.images.generate(

model= " gpt-image-1 " ,

prompt=prompt

)

The correct selection is images.generate . Microsoft’s current Foundry documentation shows that image generation with a deployed GPT Image model uses the OpenAI Python SDK ' s Images API , specifically client.images.generate(). Microsoft provides an example using model= " gpt-image-1 " together with a text prompt, which directly corresponds to this scenario.

chat.completions.create is intended for chat-completion interactions rather than the standalone Images API. images.create is not the documented Python SDK method for generating an image. responses.create belongs to the Responses API , which can also orchestrate image generation through an image-generation tool, but that is a different API pattern from the explicitly requested OpenAI Images API . Microsoft distinguishes the standalone image-generation API from image generation performed through the Responses API.

One important detail in the supplied exhibit is that it visually shows client.responses. before the dropdown. Taken literally, client.responses.images.generate() would not be the documented syntax. For this question, the intended answer is clearly images.generate , yielding the documented form client.images.generate(...) .

Study Guide Reference: Microsoft Azure AI Fundamentals → Implement AI solutions by using Microsoft Foundry → Generative AI and image-generation models.