Microsoft AI-901 Microsoft Azure AI Fundamentals (Updated Version) Exam Practice Test

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.

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.

The correct parameter is analyzer_id. The Azure AI Content Understanding Python SDK uses the ContentUnderstandingClient.begin_analyze() method to submit content for processing. Microsoft defines the method signature with analyzer_id as the required identifier that specifies which analyzer should process the supplied input.

Therefore, the code should be completed as:

poller = client.begin_analyze(analyzer_id= " invoiceAnalyzer " , ...)

A custom analyzer encapsulates the configuration that determines how Content Understanding interprets the source content and which fields or structured information it extracts. Because the analyzer has already been created with the name invoiceAnalyzer, that value is supplied through analyzer_id when the analysis operation begins.

extraction_type is not the parameter used to select an analyzer. model_name would refer conceptually to an underlying AI model, but Content Understanding analysis is invoked through an analyzer abstraction rather than by specifying a model directly. Likewise, schema_id does not identify the analyzer to execute.

Microsoft ' s custom-analyzer guidance explicitly demonstrates client.begin_analyze(analyzer_id=analyzer_id, inputs=[...]), confirming the required SDK pattern

A loan approval system that shows gender bias violates the Microsoft responsible AI principle of fairness. Fairness means AI systems should avoid unfair bias and should treat people equitably across demographic groups such as gender, race, age, and other protected or sensitive characteristics.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

When using the Azure OpenAI Responses API with a vision-enabled model, the image must be included as an input image content item. The correct content item type is:

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

Microsoft’s Azure OpenAI Responses API documentation states that the Responses API supports image inputs, and multimodal requests use structured input content items for the request.

Why the other options are incorrect:

A. image_base64 = Incorrect. Base64 data can be used as the image data format, but the content item type is still input_image.

B. image_generation = Incorrect. This is related to generating images, not providing an image for analysis.

C. output_image = Incorrect. This would refer to generated output, not image input.

D. input_image = Correct.

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.

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.

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.

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 described solution coordinates a multi-step workflow: gathering user preferences, searching for options, requesting approval, booking services, and maintaining context across steps. This is an example of Agentic AI.

Agentic AI systems use AI agents to reason over user goals, maintain context, decide which actions to take, and use tools or services to complete tasks across multiple steps.

Why the other options are incorrect:

A. information extraction extracts structured data from content.

C. computer vision analyzes images or video.

D. generative AI creates new content, but the scenario specifically describes an autonomous, context-aware workflow with actions, which is Agentic AI.

input=[

{

" role " : " user " ,

" content " : [

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

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

],

}

]

print(response.output_text)

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

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

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.

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.

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.

Statement 1: Voice Live returns only transcribed text. = No

Voice Live is not limited to transcription. Microsoft documentation states that the Voice Live API supports real-time bidirectional voice applications, including speech recognition, text-to-speech synthesis, avatar streaming, animation data, and audio processing.

Statement 2: Voice Live requires you to separately implement speech to text and text to speech services. = No

Voice Live provides a single real-time voice API experience rather than requiring separate STT and TTS implementations for the conversational loop. Microsoft describes live AI voice conversations as combining speech capabilities for real-time interaction, and the Voice Live API includes speech recognition and text-to-speech synthesis features.

Statement 3: Voice Live combines speech to text, reasoning, and text to speech into a single conversational experience. = Yes

This is correct. Microsoft’s guidance explains that Azure OpenAI audio/realtime capabilities are for scenarios that combine audio with language understanding, reasoning, or generation in a single model call, and Voice Live supports real-time voice-enabled applications over WebSocket connections.

The correct answer is endpoint of the model. After a vision-enabled GPT model is deployed in Microsoft Foundry, the deployment exposes an inference endpoint that applications can call programmatically. The application sends requests containing text and, when required, image content to the deployed model through the appropriate chat-completions API endpoint. Microsoft’s documentation explicitly states that to use vision-enabled models, you call the Chat Completion API against a supported model that has been deployed.

The Foundry playground is primarily an interactive environment for testing prompts, model behavior, and multimodal inputs during development. It is not the production interface that an external application uses to submit inference requests. An evaluation pipeline is used to assess model or application quality against defined metrics and datasets, while a training dataset is associated with model training or fine-tuning rather than serving inference traffic.

Microsoft Foundry provides the endpoint and required authentication information after deployment. Applications then invoke that endpoint using an SDK or REST API, passing text and image inputs as part of the request payload.

The system must not discriminate based on gender, race, or age. = Fairness

This requirement maps to fairness because fairness means AI systems should avoid unfair bias and treat people equitably across demographic groups.

Personal data must be visible only to approved users. = Privacy and security

This requirement maps to privacy and security because it focuses on protecting personal data and restricting access to authorized users only.

Automated decision-making processes must be recorded so that approved users can identify why a decision was made. = Transparency

This requirement maps to transparency because recording decision-making processes helps users understand and explain how or why an AI-supported decision was made.

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

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

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

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

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

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

Statement 1: 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.

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.

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 " )

Generating marketing email drafts from a short product description and target audience is a content generation task. This is a generative AI workload because the system creates new text based on the user’s prompt.

B. computer vision is for interpreting images or video.

C. text classification categorizes existing text, but does not draft new marketing emails.

D. speech recognition converts spoken audio into text.

Therefore, the correct answer is A. generative AI.