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

Statement 1: An Azure Content Understanding in Foundry Tools analyzer returns results in the HTML format. = No

Azure Content Understanding returns analysis results as structured output, typically JSON , not HTML. Microsoft documentation states that the Content Understanding API returns analysis results in a structured JSON format.

Statement 2: The Content Understanding REST API analyzes content synchronously and returns results in the same response. = No

Content Understanding analysis is asynchronous. The quickstart states that the response header includes an Operation-Location field, which is used to retrieve the results of the asynchronous analysis operation.

Statement 3: Azure Content Understanding in Foundry Tools can transform documents, images, audio, and video files into structured output. = Yes

Microsoft describes Azure AI Content Understanding as processing documents, images, videos, and audio, and transforming them into user-defined output formats.

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.

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.

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 .

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.

Voicemail recordings are audio content . Azure Content Understanding analyzers define what type of content to process, including documents, images, audio, or video , and what elements to extract, including transcripts and structured fields.

Microsoft’s custom analyzer documentation also shows an audio example based on prebuilt-audio for processing customer support call recordings, which is the same content type as voicemail recordings.

Therefore, to extract a transcript and structured information from voicemail recordings, you should use an audio analyzer .

Fairness

Privacy and security

Transparency

Reliability and safety

Comprehensive and Detailed Explanation with all Azure AI documents : =

Evaluating model outputs to ensure that decisions are NOT biased against specific demographic groups: Fairness

Microsoft’s responsible AI principle of fairness means AI systems should treat people fairly and avoid unfair bias across demographic groups.

Encrypting sensitive customer data and restricting system access to authorized personnel: Privacy and security

The privacy and security principle requires AI systems to protect personal and sensitive data and prevent unauthorized access.

Informing users when they are interacting with an AI system and explaining the system’s capabilities and limitations: Transparency

The transparency principle requires that people understand when they are interacting with AI and understand the system’s purpose, capabilities, and limitations.

Testing AI systems under different conditions to reduce unexpected failures: Reliability and safety

The reliability and safety principle means AI systems should operate reliably and safely, including through testing and validation under expected and unexpected conditions.

A system prompt, also called a system message, is used to guide how a generative AI model behaves, including its tone, role, response style, and output format. Microsoft states that system messages help steer an Azure OpenAI chat model toward the desired behavior, tone, and output format.

Statement 1: System prompts can be used to authorize users. = No

Authorization is handled by application identity, access control, authentication, and security configuration. A system prompt can guide model behavior, but it is not an authorization mechanism.

Statement 2: A system prompt is used to reduce tokens per minute (TPM). = No

TPM is a quota or throughput setting for model deployments. A system prompt does not reduce or configure TPM.

Statement 3: A system prompt guides the behavior of a generative AI model. = Yes

This is correct. Microsoft documentation says system messages help steer the model’s behavior, tone, and output format.

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.

Microsoft Foundry Agent Service documentation states that instructions define goals, constraints, and behavior for an agent. Therefore, instructions are used to guide how the generative AI model or agent should respond and behave.

Option A is correct because instructions can define constraints the model must follow.

Option B is correct because instructions can define the agent’s role and behavior.

Options C, D, and E are incorrect because Azure region, model selection, and TPM allocation are configuration or deployment/resource settings, not purposes of prompt instructions.

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 .

Named Entity Recognition (NER) is used to extract dates, quantities, and locations from text.

Named Entity Recognition identifies and categorizes entities in text, such as people, organizations, locations, dates, times, quantities, currencies, and other structured information.

The other options are incorrect:

Key phrase extraction identifies important phrases or main topics.

Language detection identifies the language of the text.

Sentiment Analysis identifies positive, negative, neutral, or mixed sentiment.

To identify the main talking points in customer feedback surveys, use key phrase extraction . This feature identifies important words and phrases in text, which is useful for summarizing themes in feedback.

When content is submitted to Azure Content Understanding in Foundry Tools, the analysis is asynchronous . This means the service does not return results immediately within the same HTTP request. Instead, it uses the standard Azure long-running operation (LRO) pattern — you call begin_analyze() to submit the content, which immediately returns a poller object, and then call poller.result() to wait for processing to complete and retrieve the structured extraction results.

Why the other options are wrong:

Synchronous is incorrect — the analysis pipeline involves multiple AI steps (OCR, speech transcription, schema mapping) that take time; a blocking synchronous call is not supported.

Returned only as unstructured plain text is incorrect — Azure Content Understanding returns richly structured JSON output with named fields mapped to your defined schema, not plain unstructured text.

Limited to OCR-only processing is incorrect — Content Understanding goes far beyond OCR; it supports document, audio, image, and video analyzers, and performs semantic field extraction using AI, not just character recognition.

This asynchronous design is consistent across all Azure AI services that perform complex, multi-step content processing.

AI systems must consistently operate as intended, even under unexpected conditions. = Reliability and safety

This description matches the Microsoft responsible AI principle of reliability and safety , which focuses on ensuring that AI systems behave consistently, safely, and as expected, including under unusual or unexpected conditions.

AI systems must protect and secure personal and business information. = Privacy and security

This description matches the Microsoft responsible AI principle of privacy and security , which focuses on protecting personal, confidential, and business-sensitive information and preventing unauthorized access or misuse.

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.

The requirement is to extract structured fields, including nested values, from invoices by using a defined schema . In Azure Content Understanding, an analyzer is the processing unit that defines how content is analyzed, what information is extracted, and how the output is structured, including JSON fields.

Microsoft’s Content Understanding document solutions documentation states that Content Understanding uses customizable analyzers to extract essential information, fields, and relationships from documents and forms. Microsoft’s quickstart also shows invoice processing with the prebuilt-invoice analyzer to extract structured data from an invoice document.

Why the other options are incorrect:

A. transcription workflow in Azure Speech is for converting audio to text, not invoice field extraction.

B. OCR-only document processing can extract text but does not meet the requirement for structured fields and nested values by schema.

D. Azure AI Search is for indexing and querying content, not defining invoice extraction schemas.

Therefore, the correct answer is C. an analyzer in Azure Content Understanding in Foundry Tools .

Requiring human reviewers to oversee AI-generated hiring recommendations and remain responsible for the final hiring decisions is an example of the Microsoft responsible AI principle of accountability .

Accountability means people and organizations remain responsible for AI systems, their decisions, and their effects. In this scenario, the AI system assists with recommendations, but humans retain oversight and responsibility for the final decision.

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 .

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 .

In Microsoft Foundry Agent Service, tool_choice controls whether and how an agent uses tools during a run.

Statement 1: Yes

Setting tool_choice to auto lets the agent/model decide whether to call a tool. This statement is true.

Statement 2: No

Setting tool_choice to none does not mean the model decides whether to call a tool. It means the model will not call a tool. This statement is false.

Statement 3: Yes

Setting tool_choice to required means the agent must call one or more tools during the run. This statement is true.

Microsoft documentation describes the tool_choice values as follows: auto allows the model to decide whether to call tools, none prevents tool calls, and required forces one or more tool calls.

Statement 1: In the Foundry playground, you can upload a local image and include text in the same message when prompting a multimodal model. = Yes

Microsoft documentation for vision-enabled chat models states that in the chat session pane, you can select the attachment button, upload an image, add a text prompt such as “Describe this image,” and then submit it.

Statement 2: When using the OpenAI Responses API and a vision-enabled model, images can be provided only as base64-encoded image data. = No

Images are not limited to base64 data. The Responses API supports image input through an image URL, and Azure OpenAI documentation also describes examples using base64 encoded image data.

Statement 3: Prompts that include images require deploying a text-only model because multimodal capabilities are handled by the application layer. = No

Image prompts require a vision-enabled multimodal model , not a text-only model. Azure OpenAI vision-capable models accept image and text inputs directly.

Speech recognition is used to recognize spoken language and convert it into written text. Therefore, the correct match is:

Speech recognition = Transcribes spoken input

Speech synthesis , also known as text to speech , is used to convert text into spoken audio. Therefore, the correct match is:

Speech synthesis = Generates spoken output

The other options are incorrect for these two tools:

Extracts entities from the output is a text analytics / language processing task.

Translates spoken input is speech translation.

Translates written output is text translation.

Azure Content Understanding in Foundry Tools uses transcription to convert speech to text from audio and video.

Transcription is the process of converting spoken audio into written text. In Azure Content Understanding, audio and video analyzers can use transcription to produce text transcripts from spoken content.

The other options are incorrect:

diarization identifies or separates speakers, such as “Speaker 1” and “Speaker 2.”

key phrase extraction identifies important phrases from text after text exists.

synthesis converts text into spoken audio, which is the opposite direction of speech-to-text.

The correct text analysis technique is Named Entity Recognition (NER) .

Microsoft defines NER as a feature that identifies and categorizes entities in unstructured text, including people, places, and organizations .

Sentiment analysis detects positive, negative, or neutral opinion. Summarization creates shorter versions of text. Key phrase extraction identifies important phrases, but it does not specifically classify mentions as people, organizations, or locations.

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.

Statement 1: A summarization model is used to transcribe spoken audio into written text. = No

Summarization creates a shorter version of existing text. Transcribing spoken audio into written text is a speech recognition / speech-to-text capability, not summarization.

Statement 2: A chat completion model can generate vector embeddings for semantic tasks. = No

Chat completion models are used to generate conversational text responses. Vector embeddings for semantic search, similarity, and retrieval tasks are generated by embedding models , not chat completion models.

Statement 3: An embedding model can generate vector representations of the provided input. = Yes

This is correct. Embedding models convert input such as text into vector representations that capture semantic meaning and can be used for tasks such as similarity search, clustering, and retrieval.

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 .