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

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.

For a local JPEG image in a Python application using the Azure OpenAI Responses API, the image should be read, base64 encoded, and sent as a data URL, such as:

{ " type " : " input_image " , " image_url " : " data:image/jpeg;base64, < base64_image_data > " }

Microsoft’s Responses API documentation shows vision-enabled requests using input_image with image_url set to a base64 data URL in the format data:image/jpeg;base64,{base64_image}. It also confirms that JPEG images are supported.

A local file path such as file:///C:/images/photo.jpg or C:\images\photo.jpg is not a valid API-accessible image input. A public or accessible HTTPS URL can be used, but the question specifically says the application sends a local JPEG image , so the correct choice is the base64 data URL.

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.

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.

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.

Statement

Answer

Generative AI models predict the next token in a sequence based on patterns learned during training.

Yes

A system prompt can be used to influence the behavior and tone of the responses generated by a generative AI model.

Yes

Increasing the Temperature parameter makes the responses generated by generative AI models more deterministic and consistent.

No

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

The first statement is Yes . Generative language models generate text autoregressively by estimating probabilities for the next token based on preceding context and patterns learned during model training. This next-token prediction mechanism is fundamental to how transformer-based generative models produce sequences of text.

The second statement is also Yes . A system prompt provides high-level instructions that establish how the model should behave. It can define role, response style, tone, constraints, and other behavioral expectations. The AI-901 study guide explicitly includes creating effective system and user prompts as part of implementing generative AI applications in Microsoft Foundry.

The third statement is No . Increasing Temperature does the opposite of making output more deterministic. Microsoft documents that higher temperature values increase randomness and output diversity, while lower values make responses more focused and predictable. At a temperature approaching zero, model selection becomes substantially more deterministic because higher-probability tokens are favored more strongly.

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 .

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.

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.

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.

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 " )

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 workloads include capabilities such as sentiment analysis, key phrase extraction, named entity recognition, language detection, and other text analytics features. In application code, these text analysis capabilities are accessed by using the Azure Text Analytics client library .

The other options are incorrect:

Azure AI Projects client library is used for Foundry project and agent-related development.

Azure OpenAI client library is used to call Azure OpenAI model deployments.

Azure Speech SDK is used for speech recognition, speech synthesis, and other speech workloads.

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 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.

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

Microsoft documentation 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 . It also includes many models across providers such as Azure OpenAI, Mistral, Meta, Cohere, NVIDIA, and Hugging Face.

The Microsoft Learn module for Foundry models also states that the model catalog is used to explore and filter models , compare models using benchmark metrics , and deploy a model to an endpoint .

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 .

The Microsoft responsible AI principle of inclusiveness means AI systems should be designed to empower and engage everyone, including people with different abilities, languages, and accessibility needs.

Therefore, designing the interface to support multiple languages and screen readers is an example of inclusiveness .

Why the other options are incorrect:

A. Provide explanations about how predictions are generated = Transparency

C. Evaluate model outputs across demographic groups to reduce bias = Fairness

D. Encrypt stored customer data and restrict access by using role-based controls = Privacy and security

Evaluating model outcomes across demographic groups to reduce bias is an example of the Microsoft responsible AI principle of fairness .

The Microsoft responsible AI principle of fairness means AI systems should treat all people fairly and avoid unfair bias or discrimination across groups.

The other options are incorrect:

accountability means people and organizations remain responsible for AI systems and their effects.

privacy and security focuses on protecting data and controlling access.

transparency means users should understand when and how AI is being used, including system capabilities and limitations.

When calling a Foundry agent programmatically by using the Microsoft Foundry SDK, the application needs the agent’s configuration values, including the agent ID . The Foundry playground provides a Code view that shows sample application code and environment variables needed to call the agent.

The other options are incorrect:

A. copy the tool name used by the agent identifies a tool, not the agent.

B. copy the model family name identifies the model family, not the agent instance.

C. copy the project name identifies the project, not the specific agent ID.

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.

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.

A multimodal model can receive a single user prompt that includes the image plus instructions. The prompt can ask for both outputs in one request, for example: “Analyze this image and provide a written summary and an audio summary.” Therefore, the minimum number of user prompts is 1 .

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.

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.

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: The Temperature parameter can be set before deploying a model. = No

temperature is an inference/request parameter used when calling or testing a deployed model. It controls randomness in generated responses. It is not a required setting for deploying the model itself.

Statement 2: During inference, the model name is used to route requests to a specific deployment. = No

In Azure OpenAI / Microsoft Foundry deployments, application requests are routed to a specific deployment name , even when the SDK parameter is called model. The underlying model name, such as gpt-4.1-mini, is not what routes the request to the deployment.

Statement 3: After a model is deployed, both code and testing tools can be used to interact with the model. = Yes

After deployment, you can test the model in Foundry playground/testing tools or call the deployment from application code by using the endpoint, deployment name, and authentication credentials.

Statement 1: Using Azure Speech in Foundry Tools for batch transcription requires live audio input from a microphone. = No

Batch transcription is used to process stored audio files, not live microphone input. Live microphone input is used for real-time speech recognition scenarios.

Statement 2: Azure Speech in Foundry Tools can analyze video frames to detect and label objects while transcribing audio. = No

Azure Speech is for speech capabilities such as speech recognition, speech synthesis, and speech translation. Detecting and labeling objects in video frames is a computer vision/video analysis capability, not an Azure Speech capability.

Statement 3: Speech translation in Azure Speech in Foundry Tools can translate spoken audio into text in another language. = Yes

Speech translation converts spoken input in one language into translated text in another language.

The output identifies text values and labels them with entity categories, such as:

now [DateTime]

students [PersonType]

Microsoft [Organization]

175 [Quantity-Number]

Today [DateTime-Date]

This is entity recognition , also known as Named Entity Recognition (NER) . NER identifies and categorizes entities in unstructured text, such as people, organizations, locations, dates, quantities, numbers, and other named or structured values.

Why the other options are incorrect:

B. translation would convert text from one language to another.

C. key phrase extraction would return important phrases or main topics, not categorized entity labels.

D. sentiment analysis would return sentiment such as positive, negative, neutral, or mixed.

Counting the number of animals in an area based on a video feed is an example of computer vision .

Computer vision is the AI workload used to analyze visual content such as images and video. Counting animals in a video feed requires detecting visual objects in video frames and analyzing them, which is a computer vision task.

Forecasting predicts future values or trends from historical data.

Knowledge mining extracts insights from large document or data collections.

Therefore, the correct answer is computer vision .

The chatbot must interpret user intent and route actions such as accepting orders, retrieving support documents, and retrieving order status. This is language understanding , because the system must understand what the user wants to do from natural language input.

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.