Microsoft Microsoft Azure AI Fundamentals AI-900 Exam Dumps: Updated Questions & Answers (April 2026)

Speech synthesis, also known as text-to-speech (TTS), is the AI workload that converts written text into spoken words. In this case, the task is to generate narration audio from provided scripts for silent instructional videos.

Speech recognition performs the opposite function — it converts speech into text. Language modeling is for text understanding and prediction (e.g., GPT). Translation converts text between languages, not from text to audio.

Therefore, the most appropriate workload, according to Microsoft’s AI-900 study material under the “Speech AI capabilities” section, is speech synthesis, which enables natural voice narration generation.

Statement

Yes / No

Providing an explanation of the outcome of a credit loan application is an example of the Microsoft transparency principle for responsible AI.

Yes

A triage bot that prioritizes insurance claims based on injuries is an example of the Microsoft reliability and safety principle for responsible AI.

Yes

An AI solution that is offered at different prices for different sales territories is an example of the Microsoft inclusiveness principle for responsible AI.

No

This question is based on the Responsible AI principles defined by Microsoft, a major topic in the AI-900: Microsoft Azure AI Fundamentals certification. The goal of Responsible AI is to ensure that artificial intelligence is developed and used ethically, safely, and transparently to benefit people and society. Microsoft’s framework defines six core principles: Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability.

Transparency Principle – YesProviding an explanation for a loan application decision clearly reflects transparency. According to Microsoft’s Responsible AI guidelines, transparency involves ensuring that users and stakeholders understand how AI systems make decisions. When a financial AI model explains why a loan was approved or denied, it promotes user trust and confidence in automated decision-making. Transparency helps individuals understand influencing factors (like income or credit score), thereby fostering ethical AI deployment.

Reliability and Safety Principle – YesA triage bot that prioritizes insurance claims based on injury severity demonstrates reliability and safety. This principle ensures that AI systems consistently operate as intended, handle data accurately, and do not cause unintended harm. For a triage bot, safety means it must correctly interpret medical or claim information and consistently provide appropriate prioritization. Microsoft emphasizes that reliable AI systems must be tested rigorously, function correctly in various scenarios, and maintain user safety at all times.

Inclusiveness Principle – NoAn AI solution priced differently for various sales territories is unrelated to inclusiveness. Inclusiveness focuses on designing AI systems that are accessible and fair to all users, including those with disabilities or from different demographic backgrounds. Price variation across territories is a business strategy, not an ethical AI inclusion concern. Hence, this statement does not align with any Responsible AI principle.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Describe features of conversational AI workloads,” the correct answer is Power Virtual Agents. This service is part of the Microsoft Power Platform and is specifically designed to enable users to create intelligent chatbots without writing any code.

Power Virtual Agents (PVA) provides a no-code/low-code environment where business users and developers can collaboratively design conversational experiences. It integrates built-in natural language processing (NLP) models to understand user intent and respond intelligently to text or speech inputs. The platform’s interface allows chatbot creators to design dialogues visually, connect to back-end data via Power Automate, and publish bots on websites, Teams, or other communication channels.

This approach is highlighted in Microsoft Learn as an ideal solution for organizations that want to deploy conversational bots quickly without requiring specialized AI or programming expertise. PVA automatically leverages Microsoft’s language understanding models, allowing it to interpret user input and map it to predefined topics or actions.

Let’s analyze the other options:

Azure Health Bot: A specialized solution for the healthcare industry that provides prebuilt medical compliance and healthcare content. It is not a general-purpose, no-code chatbot builder.

Microsoft Bot Framework: A developer-focused SDK for building highly customized bots through code, offering maximum flexibility but not no-code functionality.

Therefore, the most appropriate choice that “can be used to build no-code apps that use built-in natural language processing models” is Power Virtual Agents — the official Microsoft no-code chatbot solution for conversational AI workloads.

QnA Maker is a cloud-based API service that lets you create a conversational question-and-answer layer over your existing data. Use it to build a knowledge base by extracting questions and answers from your semistructured content, including FAQs, manuals, and documents. Answer users’ questions with the best answers from the QnAs in your knowledge base—automatically. Your knowledge base gets smarter, too, as it

continually learns from user behavior.

In a Language Understanding (LUIS) application, an utterance represents an example of what a user might say to the bot. According to Microsoft Learn – “Build a Language Understanding app”, an utterance is a sample phrase that helps train the LUIS model to recognize user intent.

In the given example — “Which act is playing on the main stage?” — the statement is an utterance that a user might say to find out about show schedules. LUIS uses utterances like this to identify the intent (the user’s goal, e.g., GetShowInfo) and to extract any entities (e.g., main stage) that provide additional details for fulfilling the request.

To clarify the other elements:

Intent: The overall purpose or action (e.g., “FindShowDetails”).

Entity: Specific information in the utterance (e.g., “main stage”).

Domain: A general subject area (e.g., entertainment, events).

Thus, “Which act is playing on the main stage?” is an utterance used to train the LUIS model to understand natural language input.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify features of Natural Language Processing (NLP) workloads and services,” the Azure Cognitive Service for Language – Question Answering capability is designed to allow applications to respond to user questions using information from a prebuilt or custom knowledge base. It relies on Natural Language Processing (NLP) to match user queries to the most relevant answers but does not directly execute queries against databases or infer user intent.

“You can use Language Service’s question answering to query an Azure SQL database.” → NOThe Question Answering feature does not connect directly to or query structured databases such as Azure SQL. Instead, it retrieves answers from unstructured or semi-structured content (FAQs, manuals, documents). Querying SQL databases would require traditional database access, not a cognitive service.

“You should use Language Service’s question answering when you want a knowledge base to provide the same answer to different users who submit similar questions.” → YESThis statement is correct and aligns exactly with Microsoft’s official documentation. Question Answering enables organizations to create a knowledge base that can automatically answer repeated or similar customer queries using natural language understanding. For instance, two users asking “How do I reset my password?” and “Can you help me change my password?” would receive the same predefined response.

“Language Service’s question answering can determine the intent of a user utterance.” → NODetermining user intent is handled by Language Understanding (LUIS) or Conversational Language Understanding, not by Question Answering. While both belong to the Language Service, Question Answering focuses on retrieving relevant answers, whereas LUIS focuses on intent detection and entity extraction.

rrect answer is C. Predicting the sale price of a house based on historical data, the size of the house, and the number of bedrooms.

In machine learning, regression is a supervised learning technique used to predict continuous numeric values. Microsoft’s AI-900 study guide defines regression models as those that estimate relationships between variables—predicting a continuous outcome variable from one or more input features.

In this case, the house sale price is a continuous numeric value, and inputs such as size, location, and number of bedrooms are the features. Common regression algorithms include linear regression, decision tree regression, and boosted regression.

Other options represent different ML workloads:

A involves segmentation by categories (classification or clustering).

B represents clustering, grouping similar items without predefined labels.

D represents computer vision, counting animals in images rather than predicting a numeric value.

Hence, the verified answer is C. Regression.

The correct answers are derived from the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Identify guiding principles for responsible AI.”

Microsoft defines six core principles of Responsible AI:

Fairness

Reliability and safety

Privacy and security

Inclusiveness

Transparency

Accountability

Each principle addresses a key ethical and operational requirement for developing and deploying trustworthy AI systems.

Reliability and safety – “AI systems must consistently operate as intended, even under unexpected conditions.”This principle ensures that AI models are dependable, robust, and perform accurately under diverse circumstances. Microsoft emphasizes that systems should be thoroughly tested and monitored to guarantee predictable behavior, prevent harm, and maintain safety. A reliable AI solution should continue to function properly when faced with unusual or noisy inputs, and fail safely when issues arise. This principle focuses on stability, testing, and dependable performance.

Privacy and security – “AI systems must protect and secure personal and business information.”This principle ensures that AI systems comply with data privacy laws and ethical standards. It protects users’ sensitive data against unauthorized access and misuse. Microsoft highlights that organizations must implement strong encryption, data anonymization, and access control mechanisms to maintain confidentiality. Protecting user data is essential to building trust and compliance with global standards like GDPR.

Other principles such as fairness and inclusiveness apply to ensuring equitable and accessible AI, but they do not directly relate to system operation or information protection.

✅ Final Answers:

“Operate as intended” → Reliability and safety

“Protect and secure information” → Privacy and security

The correct answers are A. Grounding data can be used to constrain the output of the chatbot and C. The chatbot might respond with inaccurate data.

According to the Microsoft Azure AI Fundamentals (AI-900) study material and Microsoft Learn modules on Azure OpenAI, a chatbot built with Azure OpenAI GPT-3.5 is a large language model (LLM) capable of generating natural language responses. However, these models operate based on statistical patterns learned from massive text datasets—they do not inherently guarantee factual accuracy. Hence, while GPT-based models can produce highly coherent text, they may sometimes generate inaccurate, outdated, or fabricated information (commonly referred to as “hallucinations”). This makes C correct.

Grounding data, as described in Microsoft’s Responsible AI and Azure OpenAI grounding documentation, refers to integrating trusted external data sources—such as company documents, databases, or knowledge bases—into the prompt context. This helps the model stay aligned with factual or domain-specific content, effectively constraining its output to be relevant and verifiable. Therefore, A is also correct.

Options B and D are incorrect because GPT models do not always provide accurate information, and they are not approved for critical use cases such as medical diagnosis. Microsoft’s Responsible AI principles explicitly prohibit unverified use in healthcare or other high-risk domains.

Thus, the verified answers are A and C.