AI-900 Microsoft Azure AI Fundamentals Free Practice Exam Questions (2026 Updated)

This question tests your understanding of AI workloads as described in the Microsoft Azure AI Fundamentals (AI-900) study guide. Each Azure AI workload is designed to handle specific types of data and tasks: text, images, documents, or content generation.

Extract data from medical admission forms for import into a patient tracking database → Azure AI Document IntelligenceFormerly known as Form Recognizer, this service belongs to the Azure AI Document Intelligence workload. It extracts key-value pairs, tables, and textual information from structured and semi-structured documents such as forms, invoices, and admission sheets. For medical forms, Document Intelligence can identify fields like patient name, admission date, and diagnosis and export them into structured formats for database import.

Automatically create drafts for a monthly newsletter → Generative AIThis task involves creating original written content, which is a capability of Generative AI. Microsoft’s Azure OpenAI Service uses large language models (like GPT-4) to generate human-like text, summaries, or articles. Generative AI workloads are ideal for automating creative writing, drafting newsletters, producing blogs, or summarizing reports.

Analyze aerial photos to identify flooded areas → Computer VisionComputer Vision workloads involve analyzing and interpreting visual data from images or videos. This includes detecting objects, classifying scenes, and identifying patterns such as flooded regions in aerial imagery. Azure’s Computer Vision or Custom Vision services can be trained to detect water coverage or terrain changes using image recognition techniques.

Thus, the correct matches are:

Azure AI Document Intelligence → Extract medical form data

Generative AI → Create newsletter drafts

Computer Vision → Identify flooded areas from aerial photos

The correct answer is Azure AI Language, which includes the Question Answering capability (previously known as QnA Maker). According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation, the Azure AI Language service can be used to create a knowledge base from frequently asked questions (FAQ) and other structured or semi-structured text sources.

This service allows developers to build intelligent applications that can understand and respond to user questions in natural language by referencing prebuilt or custom knowledge bases. The Question Answering feature extracts pairs of questions and answers from documents, websites, or manually entered data and uses them to construct a searchable knowledge base. This knowledge base can then be integrated with Azure Bot Service or other conversational platforms to create interactive, self-service chatbots.

Here’s how it works:

Developers upload FAQ documents, URLs, or structured content.

Azure AI Language processes the content and identifies logical question-answer pairs.

The model stores these pairs in a knowledge base that can be queried by user input.

When users ask questions, the model finds the best matching answer using natural language understanding techniques.

In contrast:

Azure AI Document Intelligence (Form Recognizer) is used to extract structured data from forms and documents, not to create FAQ knowledge bases.

Azure AI Bot Service is for managing and deploying conversational bots but does not generate knowledge bases.

Microsoft Bot Framework SDK provides tools for building conversational logic but still requires a knowledge source like Question Answering from Azure AI Language.

Therefore, the service that can create a knowledge base from FAQ content is Azure AI Language.

The correct completion of the sentence “_____ is an example of speech recognition.” is A voice-activated security key system.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Describe features of common AI workloads”, speech recognition refers to the ability of a system or application to convert spoken language into text or actionable commands. It allows computers to interpret and respond to human speech inputs, bridging human-computer interaction through natural language.

Microsoft Learn clearly explains that speech recognition is used in applications such as voice assistants, dictation software, and voice-activated security systems, where the spoken input from a user is captured, analyzed, and translated into commands or text. For example, when a user says “Unlock door” or “Open session,” the speech recognition system interprets that sound input, converts it into text or a command, and then performs the appropriate action. This is a direct implementation of speech-to-text processing combined with command execution logic.

Let’s analyze the other options:

Creating an audio commentary for a video recording is related to speech synthesis (text-to-speech), not recognition.

Creating captions for a video recording involves speech-to-text transcription, which is a subset of speech recognition, but the question emphasizes a system that responds to voice commands, making the first option more accurate.

Identifying key phrases in a video transcript involves natural language processing (NLP) techniques rather than speech recognition.

Therefore, the voice-activated security key system best represents the use of speech recognition technology because it interprets spoken commands and takes a corresponding action based on recognized speech patterns. This aligns directly with the AI-900 learning objectives where speech recognition is defined as a process that enables applications to interpret and respond to human voice input.

Generative AI focuses on creating new content rather than just analyzing existing data. As per Microsoft’s AI-900 curriculum and Azure OpenAI documentation, typical use cases include generating text, images, code, or other creative outputs based on input prompts.

A. Generating draft responses for customer service agents — ✅ Correct. GPT-based models can automatically generate draft replies to customer queries, enabling agents to refine responses and increase efficiency.

B. Creating original artwork from textual descriptions — ✅ Correct. DALL-E, available through Azure OpenAI, can produce unique images based on natural language prompts.

Options C and D are incorrect because they involve predictive or classification models, not generative ones:

C. Predicting sales revenue → Regression (machine learning).

D. Classifying email messages → Classification (machine learning).

Correct answers: A and B.

When training a machine learning model, it is standard practice to randomly split the dataset into training and testing subsets. The purpose of this is to evaluate how well the model generalizes to unseen data. According to the AI-900 study guide and Microsoft Learn module “Split data for training and evaluation”, this ensures that the model is trained on one portion of the data (training set) and evaluated on another (test or validation set).

The correct answer is C. to test the model by using data that was not used to train the model.

Random splitting prevents data leakage and overfitting, which occur when a model memorizes patterns from the training data instead of learning generalizable relationships. By testing on unseen data, developers can assess true performance, ensuring that predictions will be accurate on future, real-world data.

Options A and B are incorrect because:

A. Train the model twice does not improve accuracy; model accuracy depends on data quality, feature engineering, and algorithm choice.

B. Train multiple models simultaneously refers to model comparison, not the purpose of splitting data.

Thus, the correct reasoning is that random splitting provides a reliable estimate of the model’s predictive power on new data.

This question is drawn from the Microsoft Azure AI Fundamentals (AI-900) syllabus section “Describe features of natural language processing (NLP) workloads on Azure.” According to the Microsoft Learn materials, Natural Language Processing (NLP) is a branch of artificial intelligence that allows computers to analyze, understand, and generate human language. NLP enables machines to work with text or speech data in a way that extracts meaning, sentiment, and intent.

Microsoft defines NLP as enabling scenarios such as language detection, text classification, key phrase extraction, sentiment analysis, and named entity recognition. The example given—classifying emails as “work-related” or “personal”—is a text classification task, which falls under NLP capabilities. The AI model processes the textual content of emails, identifies linguistic patterns, and categorizes them based on the detected topic or context.

Let’s analyze the other options:

Predict the number of future car rentals → This is a forecasting task, handled by machine learning regression models, not NLP.

Predict which website visitors will make a transaction → This is a classification or prediction problem in machine learning, not NLP, since it deals with behavioral or numerical data rather than language.

Stop a process in a factory when extremely high temperatures are registered → This is an IoT or anomaly detection scenario, focusing on sensor data, not language understanding.

Therefore, only classifying email messages as work-related or personal correctly represents an NLP use case. It illustrates how NLP can analyze written text and make intelligent categorizations—a key capability covered in AI-900’s natural language workloads section.

Microsoft’s Responsible AI principles are central to the AI-900 curriculum and consist of six key tenets:

Fairness – AI systems should treat all people fairly.

Reliability and safety – AI systems should perform reliably and safely.

Privacy and security – AI systems should be secure and respect user privacy.

Inclusiveness – AI systems should empower everyone.

Transparency – AI systems should be understandable.

Accountability – People should be accountable for AI outcomes.

The statement “AI systems should be secure and respect privacy” reflects the Privacy and Security principle, which ensures AI solutions protect personal data and operate within compliance frameworks. Microsoft’s responsible AI framework emphasizes building trust by safeguarding sensitive data used in AI applications.

The other options do not align with official responsible AI principles; for example, AI systems need not “be in the public domain,” nor are they meant to prioritize developers’ interests or expose personal details. Hence, the correct and Microsoft-verified answer is C. AI systems should be secure and respect privacy.

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore conversational AI in Microsoft Azure,” to create a chatbot that can automatically answer simple, predefined user questions, you need two main Azure AI components — one to handle the conversation interface and another to manage the knowledge and language understanding aspect.

Azure Bot Service (A)This service is used to create, manage, and deploy chatbots that interact with users through text or voice. The Bot Service provides the framework for conversation management, user interaction, and channel integration (e.g., webchat, Microsoft Teams, Skype). It serves as the backbone of conversational AI applications and supports integration with other cognitive services like the Language Service.

Language Service (D)The Azure AI Language Service (which now includes Question Answering, formerly QnA Maker) is used to build and manage the knowledge base of predefined questions and answers. This service enables the chatbot to understand user queries and return appropriate responses automatically. The QnA capability allows you to import documents, FAQs, or structured data to create a searchable database of responses for the bot.

Why the other options are incorrect:

B. Azure Machine Learning: This service is used for building, training, and deploying custom machine learning models, not for chatbot Q & A automation.

C. Translator: This service performs language translation, which is not required for answering predefined questions unless multilingual support is specifically needed.

Therefore, to implement a chatbot that can answer simple, repetitive user questions and reduce the load on human operators, you combine Azure Bot Service (for interaction) with the Language Service (for question-answering intelligence).

to 300 words in Explanation:

The Custom Vision service under Azure Cognitive Services is specifically designed for image classification and object detection tasks that require a custom-trained model. According to the AI-900 official study materials, Custom Vision enables developers to “build, deploy, and improve image classifiers that recognize specific objects in images based on custom data.”

In this question, the goal is to build a system that processes images from retail stores and identifies products of competitors. Since these are unique products that may not be part of Microsoft’s pre-trained models, a custom model must be created. The Custom Vision service allows you to upload your own labeled images (e.g., product pictures), train a model to recognize those products, and then deploy it as an API for image recognition tasks.

Other options explained:

B. Form Recognizer is used to extract text, key-value pairs, and tables from structured or semi-structured documents like invoices or receipts. It is not suitable for object identification.

C. Face service detects and analyzes human faces, providing attributes like age, emotion, and facial landmarks, but cannot recognize general objects like products.

D. Computer Vision is a general-purpose image analysis service used for tagging, OCR, and scene recognition, but it uses pre-trained models. It doesn’t allow for custom product identification.

Thus, based on Microsoft’s guidance, the best fit for recognizing competitor products from images using a custom-trained model is A. Custom Vision.

The correct answer is A. Personally Identifiable Information (PII) detection.

In the Azure AI Language service, PII detection is a built-in feature designed to automatically identify and redact sensitive or confidential information from text data. According to the Microsoft Learn module “Identify capabilities of Azure AI Language” and the AI-900 study guide, this capability can detect personal data such as names, phone numbers, email addresses, credit card numbers, and other identifiers.

When applied, the service scans input text and either masks or removes these PII elements based on configurable parameters, ensuring compliance with data privacy regulations like GDPR or HIPAA.

For example, if a document contains “John Doe’s phone number is 555-123-4567,” PII detection can return “******’s phone number is ***********,” thereby preventing exposure of sensitive personal details.

Option analysis:

A. Personally Identifiable Information (PII) detection: ✅ Correct. It identifies and masks sensitive data in text.

B. Entity linking: Connects recognized entities to known data sources like Wikipedia; not used for redaction.

C. Custom text classification: Classifies text into predefined categories; not designed for masking personal data.

D. Custom named entity recognition (NER): Detects domain-specific entities you define but doesn’t automatically mask them.

Therefore, to automate masking of names and phone numbers, the appropriate Azure AI Language feature is PII detection.

Conversational AI workloads involve human-like dialogue with AI systems.

A: A smart assistant (e.g., smart speaker) uses voice-based conversational AI.

B: A knowledge-based chatbot interacts with users via natural language.Options C and D describe automation/IoT workloads, not conversational AI.

✅ Final Answer (Q110): A and B

In a chat solution using the Azure OpenAI GPT-3.5 model, the temperature parameter controls the creativity and variability of generated responses. According to the Microsoft Learn documentation for Azure OpenAI Service, temperature is a float value typically between 0 and 2, determining how deterministic or random the model’s output is. A lower temperature (e.g., 0–0.3) makes responses more focused and deterministic, while a higher temperature (e.g., 0.8–1.2) produces more verbose, creative, and diverse responses.

When you want the chat model to generate more detailed or expressive output, increasing the temperature encourages the model to explore a broader range of possible continuations, leading to longer and more varied text. This parameter directly affects how “verbose” or elaborate the model’s responses can be, which is why it is the correct answer.

The other options are not appropriate for this scenario:

A. Presence penalty reduces repetition by discouraging reuse of the same phrases but does not control verbosity.

C. Stop sequence defines tokens where generation should stop, limiting rather than extending response length.

D. Max response (max tokens) controls the maximum length of the response but does not inherently make answers more verbose or expressive.

Thus, to encourage more elaborate and detailed output from the Azure OpenAI GPT-3.5 model, the correct configuration parameter to adjust is Temperature (B).

facial analysis.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Describe features of computer vision workloads on Azure,” facial analysis is a computer vision capability that detects faces and extracts attributes such as facial expressions, emotions, pose, occlusion, and image quality factors like exposure and noise. It does not identify or verify individual identities; rather, it interprets facial features and image characteristics to analyze conditions in an image.

In this question, the AI solution helps photographers take better portrait photos by providing feedback on exposure, noise, and occlusion — tasks directly linked to facial analysis. The model analyzes the detected face to determine if the image is well-lit, clear, and unobstructed, thereby improving photo quality. These capabilities are part of the Azure Face service in Azure Cognitive Services, which includes both facial detection and facial analysis functionalities.

Here’s how the other options differ:

Facial detection only identifies that a face exists in an image and provides its location using bounding boxes, without further interpretation.

Facial recognition goes a step further — it attempts to identify or verify a person’s identity by comparing the detected face with stored images. This is not what the scenario describes.

Thus, when an AI solution evaluates image quality aspects like exposure, noise, and occlusion, it’s performing facial analysis, which focuses on understanding image and facial characteristics rather than identification.

In summary, based on Microsoft’s AI-900 study material, this scenario demonstrates facial analysis, a subcategory of computer vision tasks within Azure Cognitive Services.

The correct answers are A. Create images and D. Modify images.

The Azure OpenAI DALL-E model is a text-to-image generative AI model that can create original images and modify existing ones based on text prompts. According to Microsoft Learn and Azure OpenAI documentation, DALL-E interprets natural language descriptions to produce unique and creative visual content, making it useful for design, illustration, marketing, and educational applications.

Create images (A) – DALL-E can generate new images entirely from textual input. For example, the prompt “a futuristic city skyline at sunrise” would result in a custom-generated artwork that visually represents that description.

Modify images (D) – DALL-E also supports inpainting and outpainting, allowing users to edit or expand existing images. You can replace parts of an image (for example, changing a background or object) or add new elements consistent with the visual style of the original.

The remaining options are incorrect:

B. OCR is performed by Azure AI Vision, not DALL-E.

C. Detect objects in images is also an Azure AI Vision (Image Analysis) feature.

E. Generate captions for images is handled by Azure AI Vision, not DALL-E, since DALL-E generates—not interprets—visuals.

According to Microsoft’s AI-900 study guide and the Microsoft Learn module “Identify common types of AI workloads”, computer vision is the branch of AI that enables machines to interpret and act upon visual inputs such as photos, videos, or camera feeds.

In this scenario, the recycling machine must identify bottles based on their shape and reject other objects. This requires the AI system to visually analyze an image or a video stream and make a decision based on shape recognition—a classic computer vision application.

Computer vision models are trained with labeled image data (e.g., images of correct bottle shapes vs. incorrect items). Using techniques such as object detection or image classification, the model learns to detect patterns and identify whether an image matches the desired object.

The other options are not suitable:

A. Anomaly detection identifies unusual data patterns, not visual features.

B. Conversational AI handles human-computer interactions via text or voice.

D. Natural language processing (NLP) deals with understanding and generating human language.

Therefore, because the task requires visual recognition of bottle shapes, the appropriate AI workload is Computer Vision.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Describe core concepts of machine learning on Azure”, labeling is the process of assigning correct output values (labels) to training data before model training. In supervised learning, every input in the dataset must be paired with its corresponding output so the algorithm can learn the relationship between the two.

In this scenario, the task is to assign classes to images before training a classification model—for example, marking images as “cat,” “dog,” or “bird.” This process defines the target variable (label) that the model will later predict. During training, the classification model uses these labeled examples to learn patterns and distinguish between categories.

Microsoft’s official materials clearly define labeling as:

“The process of tagging data with the correct answer so that the model can learn to make predictions.”

Labeling is a crucial early step in the machine learning lifecycle, especially for image classification and natural language processing (NLP) tasks. Without accurate labels, the model cannot learn correctly and its predictions will be unreliable.

Let’s briefly clarify why the other options are incorrect:

Evaluation refers to testing the model after training to measure accuracy or performance using metrics like precision, recall, or F1 score.

Feature engineering involves creating or selecting the most relevant input features from raw data but does not involve tagging output labels.

Hyperparameter tuning adjusts parameters (like learning rate or depth of a tree) to optimize model performance after labeling and training have begun.

Thus, assigning classes to images prior to model training is definitively a Labeling task.

In the Microsoft Azure AI Fundamentals (AI-900) curriculum, language modeling is described as a core component of Natural Language Processing (NLP) that enables an AI system to understand, interpret, and generate human language in context. The AI-900 Microsoft Learn module “Identify features of Natural Language Processing workloads” explains that language modeling is used to analyze user input and determine intent — essential for conversational systems like chatbots.

In this question, the chatbot must:

Accept customer orders.

Retrieve support documents.

Retrieve order status updates.

All these tasks require the bot to understand user intent and context from text input. This understanding process is driven by language modeling, which predicts meaning and structure within sentences, enabling the system to decide what action to take next.

Microsoft Learn distinguishes between various NLP techniques:

Sentiment analysis detects emotional tone (positive/negative/neutral).

Translation converts text between languages.

Named Entity Recognition (NER) identifies specific entities like names or dates.However, none of these individually allow a system to process commands, requests, or user intents — that capability is part of language modeling, which powers LUIS (Language Understanding Intelligent Service) or the modern Azure Cognitive Service for Language.

Therefore, to build a chatbot that can interpret commands and respond contextually — such as processing orders or retrieving documents — you must use language modeling.

The Azure AI Language Service includes several natural language processing features, such as question answering, language understanding, entity recognition, sentiment analysis, and more. Each feature serves a distinct purpose, and understanding their differences is key to selecting the correct AI workload.

“You can use Azure AI Language Service ' s question answering to query an Azure SQL database.” — NOThe question answering feature is designed to retrieve answers from text-based knowledge sources (for example, FAQs, documents, or website content). It cannot directly query a database such as Azure SQL. Querying databases requires Azure Cognitive Search, Azure OpenAI, or custom integration using application logic, not the question answering model.

“You should use Azure AI Language Service ' s question answering when you want a knowledge base to provide the same answer to different users who submit similar questions.” — YESThis is the primary use case of question answering. It allows developers to build a knowledge base (KB) of predefined question-answer pairs or extract answers from documents. When users submit semantically similar questions (e.g., “What are your office hours?” or “When are you open?”), the service returns the same consistent answer.

“Azure AI Language Service ' s question answering can determine the intent of a user utterance.” — NODetermining user intent is part of the Language Understanding (LUIS) capability, not question answering. LUIS models map natural language inputs to intents and entities, typically used in bots or applications that execute tasks (like booking a meeting or checking weather).

Hence, correct answers are: No, Yes, No — aligning with the AI-900 official study guide and Microsoft Learn module “Identify Azure AI Language capabilities.”