Free Practice Questions for Microsoft AI-901 Exam

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.

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.

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.

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 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.

Providing users with clear information about an AI system’s purpose, functionality, and limitations is an example of the Microsoft responsible AI principle of transparency. Transparency means users should be able to understand when and how AI is being used, what the system can do, and what its limitations are.

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.