Practice Free AI-901 Microsoft Azure AI Fundamentals (Updated Version) Exam Questions Answers With Explanation

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 .

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.

The correct answer is Creating captions for a video recording . Speech recognition, also called speech-to-text , converts spoken audio into textual output. Microsoft explicitly defines Azure Speech-to-Text as speech recognition technology that transcribes audio streams or prerecorded audio into text.

Caption generation is a direct application of this capability. Microsoft describes captioning as converting the audio content of a video, film, webcast, or other production into text and displaying that text visually. Therefore, creating captions from the spoken content of a video is a canonical speech-recognition workload.

Creating an audio commentary is instead associated with speech synthesis or text-to-speech because the required output is spoken audio. Identifying key phrases in a video transcript is a text-analysis/NLP task performed after the speech has already been transcribed. A voice-activated security system may involve voice authentication, speaker recognition, or command recognition, but it is not as unambiguously a speech-to-text workload as caption generation.

The AI-901 Study Guide specifically requires candidates to distinguish the features and capabilities of speech recognition and speech synthesis .

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.

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 .

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.

Statement 1: Evaluators in Microsoft Foundry replace the need for configuring token limits. = No

Evaluators are used to assess model or agent output quality, safety, and reliability. They do not replace model configuration settings such as token limits. Microsoft describes evaluators as tools that measure the quality, safety, and reliability of AI responses.

Statement 2: Evaluators in Microsoft Foundry can assess the quality and safety of responses generated by a generative AI model. = Yes

This is correct. Microsoft Foundry includes built-in evaluators for general quality metrics such as coherence and fluency, safety/security metrics, RAG metrics, and agent-specific metrics.

Statement 3: Evaluators in Microsoft Foundry can retrain a deployed generative AI model automatically when quality issues are detected. = No

Evaluators measure and report quality, safety, and reliability issues. They do not automatically retrain deployed generative AI models. Microsoft describes evaluation as measuring model or agent performance against test data, while monitoring can alert when outputs fail quality thresholds.

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.

The completed sentence is:

Information extraction solutions that detect and read text in scanned documents and images rely on computer vision .

Detecting and reading text in scanned documents and images is typically done with OCR , which is a computer vision capability. Microsoft describes OCR as text recognition/text extraction that extracts printed or handwritten text from images and documents.

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.

Statement 1: In the new Microsoft Foundry portal, you must fine-tune a model before you can deploy the model. = No

Fine-tuning is optional. Microsoft’s Foundry model deployment documentation describes deploying Foundry Models directly from the model catalog for inference. It does not require fine-tuning first.

Statement 2: In the new Microsoft Foundry portal, you can test a model from the model catalog only after you deploy the model. = No

Microsoft documentation states that some Foundry Tools are available to try via the model catalog without a project , and Foundry playgrounds are used for prototyping and validation before production. Therefore, the statement using “only after you deploy” is too restrictive.

Statement 3: In the new Microsoft Foundry portal, you can deploy a model from the model catalog only after retraining the model. = No

Retraining/fine-tuning is not required before deployment. Microsoft states that after you deploy a Foundry Model, you can interact with it in the Foundry Playground and use it from code, and the deployment workflow starts by selecting a model from the model catalog and choosing Deploy .

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 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 .

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.

Use a provisioned deployment when the solution requires a specific allocated amount of throughput.

Microsoft states that provisioned throughput is a deployment type that lets you specify the amount of throughput required for a model deployment. Unlike standard deployments, where capacity is shared and throughput can vary, a provisioned deployment holds a fixed amount of processing capacity for the deployment.

Why the other options are incorrect:

A. global batch is for asynchronous batch processing, not dedicated real-time throughput allocation.

B. data zone standard and D. standard use shared capacity; they do not allocate a fixed, specified amount of throughput to the deployment.

The completed code is:

poller = client.begin_analyze(

analyzer_id= " invoice " ,

input_url=url

)

result = poller.result()

Azure Content Understanding analysis uses a long-running operation pattern. The Python SDK returns a poller from begin_analyze(), and Microsoft documentation states that the SDK poller handles polling automatically when you call .result() .

Therefore, to retrieve the analysis results after processing completes, the correct option is:

result

The other options are incorrect because status checks operation state, wait waits without returning the final analysis object, and get_results is not the method shown for retrieving the begin_analyze() result in this code pattern.

When using the OpenAI Responses API and a vision-enabled model, you can include an image in a request by providing the image as a base64-encoded image data .

Azure OpenAI / Microsoft Foundry vision requests support image input as an input_image item. The image can be provided by URL or as base64-encoded image data in the request. Microsoft documentation for Azure OpenAI batch examples also lists Base64 encoded image and Image URL as supported input patterns for Responses API requests.

The other options are incorrect:

a CSV file attachment is not an image input format for vision analysis.

an MP4 video stream is video content, not an image format.

a shared access signature (SAS) token is only an access token for a protected resource; it is not the image data itself.

Azure Text Analytics client library

Comprehensive and Detailed Explanation with all Azure AI documents : =

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 AI Language text analysis features, such as sentiment analysis, named entity recognition, key phrase extraction, language detection, and text summarization, are accessed in code 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 workflows, not specifically Azure Language text analysis.

Azure OpenAI client library is used to call OpenAI model deployments, not Azure Language text analytics APIs.

Azure Speech SDK is used for speech-to-text and text-to-speech workloads, not text analysis.

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: 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.

Statement

Answer

Image analysis capabilities in Microsoft Foundry Tools can perform optical character recognition (OCR).

Yes

Image analysis capabilities in Microsoft Foundry Tools can generate captions and descriptive tags for images.

Yes

Image analysis capabilities in Microsoft Foundry Tools are designed primarily to create new images from text prompts.

No

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

The correct sequence is Yes, Yes, No .

The first statement is Yes . Azure Vision Image Analysis in Microsoft Foundry Tools supports optical character recognition (OCR) . Microsoft documents Image Analysis 4.0 as supporting synchronous OCR through its Read capability, which extracts printed or handwritten text contained within images.

The second statement is also Yes . Image Analysis provides both captioning and image tagging . Captions generate a human-readable description of image content, while tags identify recognizable objects, scenes, actions, and other visual concepts. Microsoft explicitly lists capabilities such as Captions, Dense Captions, Tags, Object Detection, People detection, and Read/OCR as Image Analysis features.

The third statement is No . Image Analysis is primarily concerned with extracting information from existing images , not generating new images. Creating an image from a textual prompt is a generative AI/image-generation workload, typically performed by image-generation models. Image Analysis instead interprets visual input by extracting text, captions, tags, objects, and related information.

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.

To compare the costs of large language models in Microsoft Foundry portal, use the Model leaderboard .

Microsoft documentation states that the model leaderboard helps compare models across quality, safety, estimated cost, and throughput . It also supports trade-off charts and side-by-side model comparison for features, performance, and estimated cost.

Why the other options are incorrect:

A. Evaluator catalog is for selecting evaluators to measure model or application outputs, not comparing LLM costs.

C. Compliance relates to governance and compliance, not model cost comparison.

D. Tools provides Foundry tools, not benchmarked cost comparison across models.

The correct selection is AIProjectClient . In the Microsoft Foundry SDK, AIProjectClient serves as the primary project-level client for accessing and managing resources associated with a Foundry project. Microsoft documentation specifically exposes operation groups through this client for deployments , agents , and indexes , which directly matches the requirements stated in the question.

For example, the SDK exposes deployments operations for working with models deployed to the project, agents operations for creating and managing agents, and indexes operations for accessing project search indexes. Microsoft also describes the Azure AI Projects client library as a unified Foundry SDK component that connects applications to project resources through a single project endpoint.

ChatCompletionsClient is focused primarily on model inference and chat-completion operations rather than comprehensive project-resource management. FoundryLocalManager is associated with local Foundry capabilities, while ModelCatalogClient would relate to model discovery/catalog functionality rather than deployments, agents, and indexes collectively.

Therefore, the class required for this scenario is AIProjectClient .

For vision-enabled Azure OpenAI / Microsoft Foundry model requests, image input can be provided by using an image URL or base64-encoded image data. Microsoft’s Azure OpenAI REST API reference states that the image content part URL field can contain either a URL of the image or the base64 encoded image data . It also states that the Responses API input_image.image_url value can be a fully qualified URL or a base64 encoded image in a data URL .

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.

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.