Free Microsoft AI-901 Practice Exam with Questions & Answers

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.

No

Yes

No

Comprehensive and Detailed Explanation with all Azure AI documents: =

Statement 1: Speech synthesis converts spoken audio into written text. = No

Speech synthesis, also called text to speech, converts written text into spoken audio. Converting spoken audio into written text is speech recognition or speech to text.

Statement 2: Speech recognition can detect and transcribe spoken input. = Yes

Speech recognition is used to identify spoken language in audio and convert it into text.

Statement 3: Text to speech in Azure Speech in Foundry Tools can translate text input into audible speech in a different language. = No

Text to speech converts text into spoken audio. Translation into another language is a separate translation capability, not standard text-to-speech synthesis.

Statement 1: Azure Content Understanding in Foundry Tools can analyze only PDF documents. = No

Azure Content Understanding is not limited to PDF documents. It can analyze multiple content types, including documents, forms, images, audio, and video.

Statement 2: Azure Content Understanding in Foundry Tools results are returned in the JSON format. = Yes

Azure Content Understanding returns structured analysis results in JSON format. This is how extracted fields, values, confidence scores, and other analysis results are represented.

Statement 3: Azure Content Understanding in Foundry Tools can extract structured fields from documents and forms. = Yes

Azure Content Understanding can use analyzers and schemas to extract structured fields from documents and forms, such as invoices, receipts, contracts, and other business 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.

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.

Correct selection: natural language processing

The correct answer is natural language processing . Interactive question answering requires an application to receive language from a user, interpret the meaning or intent of that language, and generate or retrieve an appropriate response. These are core capabilities associated with natural language processing (NLP) and modern generative AI systems.

Microsoft’s current Azure AI Fundamentals study guide places text-oriented capabilities under AI workloads , including generative and agentic AI, text analysis, speech, computer vision, and information extraction . It also requires candidates to recognize scenarios involving text processing and to implement AI solutions that work with textual user input.

Computer vision is concerned with understanding or generating visual information such as images and video. It would therefore apply to tasks such as image classification, object detection, OCR-related visual processing, or image interpretation—not ordinary text-based question answering.

Forecasting predicts future numerical or categorical values from historical data, such as future sales, demand, or resource usage. It does not describe conversational interaction with users.

Therefore, an application that interactively receives questions expressed in natural language and returns answers is fundamentally performing a natural language processing workload .

Study Guide Reference: Identify AI concepts and capabilities → Identify AI workloads → text analysis and generative AI scenarios.

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.

Azure Content Understanding analyzers define the type of content to process, such as documents, images, audio, or video, and what to extract, such as text, layout, fields, transcripts, and structured JSON output. Microsoft lists base analyzer types such as prebuilt-document, prebuilt-audio, prebuilt-video, and prebuilt-image.

For scanned invoices in PDF format, the correct choice is document analyzer, because invoices are document/form content. Microsoft’s Content Understanding quickstart specifically uses the prebuilt-invoice analyzer to extract structured data from an invoice PDF.

For voicemail recordings in WAV format, the correct choice is audio analyzer, because voicemail is audio content. Content Understanding supports structured data extraction from audio files, and the analyzer reference identifies prebuilt-audio as the base analyzer for audio content.