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

In Conversational Language Understanding (CLU), a core service within Azure AI Language, intents represent the goals or purposes behind user utterances (for example, “Track my order” or “Cancel my subscription”). However, in real-world scenarios, users often provide inputs that do not match any defined intent. To handle such cases gracefully, Microsoft recommends including a “None” intent that captures out-of-scope utterances — text that doesn’t belong to any other intent in your model.

According to the Microsoft Learn module: “Build a Conversational Language Understanding app”, the None intent serves as a catch-all or fallback category for utterances that the model should ignore or respond to with a default message (e.g., “I’m sorry, I don’t understand that.”). By training the model with multiple examples of irrelevant or unrelated utterances in this intent, you improve its ability to distinguish between valid and invalid user inputs.

The other options are incorrect:

A. Export the model: Exporting only saves or transfers the model; it does not influence how the model detects irrelevant utterances.

B. Create a new model: A new model would not inherently solve out-of-scope detection unless properly trained with a None intent.

D. Create a prebuilt task entity: Entities identify specific data (like dates or products) within valid intents, not irrelevant utterances.

Thus, the correct approach to ensure that your CLU model can detect utterances outside its intended scope is to add examples of unrelated or off-topic phrases to the None intent. This improves classification accuracy and prevents incorrect intent matches.

✅ Correct Answer: C. Add utterances to the None intent

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Describe features of common AI workloads”, clustering is a type of unsupervised machine learning used to group data points that share similar characteristics. In unsupervised learning, the data provided to the model does not have predefined labels or outcomes. Instead, the algorithm identifies inherent patterns or groupings within the dataset based on similarities in input features.

In this scenario, the task is to identify groups of people who have similar purchasing habits. There is no predefined label such as “buyer type” or “purchase category.” The goal is to discover hidden patterns—such as grouping customers by spending behavior, preferred products, or frequency of purchases. This is precisely what clustering algorithms are designed to do.

Clustering is commonly used in:

Customer segmentation for marketing analytics.

Market basket analysis to find associations in purchasing patterns.

Recommender systems to identify similar user profiles.

Anomaly detection when outliers deviate from natural clusters.

Typical algorithms for clustering include K-means, Hierarchical clustering, and DBSCAN. These models analyze multidimensional data to form clusters that maximize intra-group similarity and minimize inter-group similarity.

By contrast:

Classification (A) is a supervised learning method that predicts a categorical label (e.g., whether a customer will churn or not). It requires labeled training data.

Regression (B) is used to predict continuous numeric values (e.g., sales revenue, temperature).

Since the question focuses on discovering groups of similar customers without prior labels, the correct type of machine learning is Clustering.

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, which are part of the AI-900 Microsoft Azure AI Fundamentals curriculum. Microsoft’s Responsible AI framework consists of six key principles: Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability. Each principle ensures that AI systems are developed and used in a way that benefits people and society responsibly.

Transparency Principle – YesProviding an explanation for a loan decision aligns with the Transparency principle. Microsoft defines transparency as helping users and stakeholders understand how AI systems make decisions. For example, when a credit scoring AI model approves or denies a loan, explaining the factors that influenced that outcome (such as credit history or income level) ensures that customers understand the reasoning process. This builds trust and supports responsible deployment.

Reliability and Safety Principle – YesA triage bot that prioritizes insurance claims based on injury severity relates directly to Reliability and Safety. This principle ensures AI systems operate consistently, perform accurately, and produce dependable outcomes. In the case of the triage bot, it must reliably assess the input data (injury descriptions) and rank claims appropriately to avoid harm or misjudgment, aligning with Microsoft’s emphasis on designing AI systems that are safe and robust.

Inclusiveness Principle – NoAn AI solution priced differently across sales territories is not related to Inclusiveness. Inclusiveness focuses on ensuring accessibility and eliminating bias or exclusion for all users—especially those with disabilities or underrepresented groups. Pricing strategy is a business decision, not an inclusiveness issue. Therefore, this statement is No.

In summary, based on the AI-900 Responsible AI principles, the correct selections are:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Describe features of natural language processing (NLP) workloads on Azure,” Natural Language Processing refers to the branch of AI that enables computers to interpret, understand, and generate human language. One of the main NLP workloads identified by Microsoft is speech-to-text conversion, which transforms spoken words into written text.

Creating a text transcript of a voice recording perfectly fits this definition because it involves converting audio language data into text form — a process handled by speech recognition models. These models analyze the acoustic features of human speech, segment phonemes, identify words, and produce a text transcript. On Azure, this function is implemented using the Azure Cognitive Services Speech-to-Text API, part of the Language and Speech services.

Let’s examine the other options to clarify why they are incorrect:

Computer vision workload: Involves interpreting and analyzing visual data such as images and videos (e.g., object detection, facial recognition). It does not deal with speech or audio.

Knowledge mining workload: Refers to extracting useful information from large amounts of structured and unstructured data using services like Azure Cognitive Search, not transcribing audio.

Anomaly detection workload: Involves identifying unusual patterns in data (e.g., fraud detection or sensor anomalies), unrelated to language or speech.

In summary, when a system creates a text transcript from spoken audio, it is performing a speech recognition task—classified under Natural Language Processing (NLP). This workload helps make spoken content searchable, analyzable, and accessible, aligning with Microsoft’s Responsible AI goal of enhancing accessibility through language understanding.

The correct answer is object detection. According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module “Explore computer vision”, object detection is the process of identifying and locating objects within an image or video. The primary characteristic of object detection, as emphasized in the study guide, is its ability to return a bounding box around each detected object along with a corresponding label or class.

In this question, the task involves returning a bounding box that indicates the location of a vehicle in an image. This is the exact definition of object detection — identifying that the object exists (a vehicle) and determining its position within the frame. Microsoft Learn clearly differentiates this from other computer vision tasks. Image classification, for example, only determines what an image contains as a whole (for instance, “this image contains a vehicle”), but it does not indicate where in the image the object is located. Optical character recognition (OCR) is specifically used for extracting printed or handwritten text from images, and semantic segmentation involves classifying every pixel in an image to understand boundaries in greater detail, often used in autonomous driving or medical imaging.

The official AI-900 guide highlights object detection as one of the key computer vision workloads supported by Azure Computer Vision, Custom Vision, and Azure Cognitive Services. These services are designed to detect multiple instances of various object types in a single image, outputting bounding boxes and confidence scores for each.

Therefore, based on the AI-900 official curriculum and Microsoft Learn concepts, returning a bounding box that shows the location of a vehicle is a textbook example of object detection, as it involves both recognition and localization of the object within the image frame.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Explore natural language processing (NLP) in Azure”, key phrase extraction is a core feature of the Azure AI Language Service that enables you to automatically identify the most important ideas or topics discussed in a body of text.

When analyzing customer feedback surveys, key phrase extraction helps summarize the main talking points or recurring themes by detecting significant words and phrases. For instance, if multiple customers write comments like “The checkout process is slow” or “Website speed could be improved,” the model may extract key phrases such as “checkout process” and “website speed.” This allows businesses to quickly understand the most common subjects without manually reading each response.

Let’s review the other options:

A. Language detection: Determines the language of the text (e.g., English, French, or Spanish) but does not identify main ideas.

B. Translation: Converts text from one language to another using Azure Translator; it does not summarize or extract key information.

C. Entity recognition: Identifies named entities such as people, organizations, locations, or dates. While useful for identifying specific details, it does not capture general topics or overall discussion points.

Therefore, the appropriate NLP feature for identifying main topics or themes within textual data such as survey responses is Key Phrase Extraction.

This capability is part of the Azure AI Language Service and is commonly used in sentiment analysis pipelines, customer feedback analytics, and business intelligence workflows to summarize large text datasets efficiently.

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore fundamental principles of machine learning,” anomaly detection is a machine learning workload used to identify data points or patterns that deviate significantly from expected behavior.

In this scenario, the IoT device monitors engine temperature and generates alerts when the readings deviate from normal operating ranges. This directly matches the definition of anomaly detection, where the AI system learns what “normal” looks like and identifies outliers or abnormal conditions that may indicate potential issues.

Common real-world uses of anomaly detection include:

Detecting equipment malfunctions or overheating in IoT systems.

Identifying fraudulent transactions in finance.

Detecting unusual spikes or drops in system metrics (e.g., temperature, traffic, or pressure).

Other options are incorrect:

A. NLP (Natural Language Processing): Focuses on understanding and interpreting human language, not sensor data.

B. Computer Vision: Involves analyzing images or videos, which is unrelated to temperature data.

D. Knowledge Mining: Refers to extracting information from large document stores, not identifying abnormal readings.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Explore fundamental principles of machine learning”, feature engineering is the process used to generate additional features or transform existing data into forms that improve model performance. Features are individual measurable properties or characteristics used as input for machine learning algorithms. The goal of feature engineering is to create new informative variables that better represent the underlying patterns in the data.

Feature engineering may include tasks such as:

Combining or transforming raw data columns (e.g., creating a “total purchase amount” from price × quantity).

Extracting time-based components (e.g., year, month, day, hour) from datetime values.

Encoding categorical variables (e.g., one-hot encoding or label encoding).

Scaling or normalizing numerical features.

Creating polynomial or interaction terms to capture complex relationships.

Microsoft’s AI-900 learning material emphasizes that the process of preparing data for machine learning involves data cleaning, feature engineering, and feature selection. While feature selection is about choosing the most relevant features from the existing dataset, feature engineering focuses on creating or generating new features to enhance model accuracy and generalization.

The other options do not fit this definition:

Feature selection is about removing redundant or irrelevant features, not generating new ones.

Model evaluation involves assessing the model’s performance using metrics like accuracy or F1 score.

Model training is the phase where the algorithm learns patterns from the data, not when features are created.

Therefore, based on the AI-900 official concepts and Microsoft’s documentation, the correct answer is Feature engineering, as it is the process specifically used to generate additional features that improve machine learning model performance and predictive capability.

The Azure OpenAI Service provides access to advanced Generative Pre-trained Transformer (GPT) models developed by OpenAI, such as GPT-3, GPT-3.5, and GPT-4. These models are capable of performing a wide range of natural language processing (NLP) and generative AI tasks — including text completion, summarization, translation, question answering, content creation, and code generation.

According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation for Azure OpenAI, this managed service allows developers to deploy and integrate GPT-based models within their own applications using REST APIs or the Azure SDK. The service handles scalability, performance, and infrastructure automatically, meaning users do not need to manage servers or computational resources manually.

Option review:

Supports the deployment of GPT-based models — ✅ Correct. Azure OpenAI is specifically designed for deploying and operationalizing GPT models and similar transformer-based architectures.

Provides capabilities exclusively for vision-related tasks — ❌ Incorrect. Vision tasks (like image classification or object detection) are part of Azure AI Vision.

Provides capabilities exclusively for speech-related tasks — ❌ Incorrect. Speech processing (speech-to-text, text-to-speech, translation) belongs to Azure AI Speech Services, not Azure OpenAI.

Requires manual infrastructure management for scalability — ❌ Incorrect. Azure OpenAI is a fully managed service; scalability and performance are handled automatically by Azure.

Therefore, the correct completion of the sentence is:

“Azure OpenAI Service supports the deployment of GPT-based models.”

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 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.

Azure Kubernetes Service (AKS).

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn documentation on Azure Machine Learning, the Azure Kubernetes Service is commonly used to host and deploy machine learning models, including Automated ML models, into production environments. Once a model is trained using Azure Machine Learning (Azure ML), it must be deployed as a web service endpoint so it can receive data and return predictions.

Azure ML offers two primary options for hosting and deploying models:

Azure Kubernetes Service (AKS) – for high-scale, production-grade deployments that require fast response times, high availability, and scalability.

Azure Container Instances (ACI) – for testing or low-scale workloads where cost and simplicity are more important than performance.

AKS provides a managed Kubernetes cluster that allows for automated container orchestration, load balancing, scaling, and monitoring of deployed machine learning models. When you use Automated ML in Azure ML Studio, the generated model can be containerized and deployed directly to AKS, making it accessible as a REST API endpoint. This enables applications, systems, or users to send data and receive predictions in real time.

The other options serve different purposes:

Azure Data Factory is used for data integration and pipeline orchestration, not model hosting.

Azure Automation focuses on automating administrative tasks and runbooks, not ML deployment.

Azure Logic Apps is used to automate workflows and integrate services, not to serve ML models.

Therefore, the correct service to host automated machine learning (AutoML) models in production is Azure Kubernetes Service (AKS), as it provides a reliable, scalable, and secure environment for real-time inference and enterprise AI workloads.

Statements

Yes

No

A webchat bot can interact with users visiting a website.

Yes

Automatically generating captions for pre-recorded videos is an example of natural language processing.

No

A smart device in the home that responds to questions such as “What will the weather be like today?” is an example of natural language processing.

Yes

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn modules on AI workloads, each of these statements maps to a distinct area of artificial intelligence — namely Conversational AI, Speech AI, and Natural Language Processing (NLP).

“A webchat bot can interact with users visiting a website.” – YesThis is true. A webchat bot represents an example of Conversational AI. It leverages natural language understanding (NLU) to interpret user input and generate appropriate responses. These bots can be created using Azure services such as Azure AI Bot Service and Language Understanding (LUIS). They enable automated interactions with users through text-based communication on websites, applications, or messaging platforms.

“Automatically generating captions for pre-recorded videos is an example of natural language processing.” – NoThis is false. Generating captions from audio involves speech recognition, not NLP. Specifically, it uses speech-to-text technology to transcribe spoken words into written text. This function is typically performed by Azure’s Speech service, which is part of the Speech AI workload, not the language-processing workload.

“A smart device in the home that responds to questions such as ‘What will the weather be like today?’ is an example of natural language processing.” – YesThis is true. Smart assistants like Alexa or Cortana use NLP to interpret spoken queries, extract meaning, and generate appropriate responses. NLP allows these devices to understand human language, retrieve relevant information, and respond conversationally.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Explore computer vision”, Optical Character Recognition (OCR) is a form of computer vision that enables a system to detect and extract printed or handwritten text from images or documents. OCR is particularly useful in scenarios where the goal is to digitize textual information from physical documents, such as receipts, invoices, or travel expense forms — exactly as described in this question.

In the given scenario, employees need a mobile application that allows them to scan and store expenses while traveling. The process involves taking photos of receipts that contain printed text, such as vendor names, totals, dates, and item descriptions. The OCR technology automatically detects the text areas within the image and converts them into machine-readable and searchable data that can be stored in a database or processed further for expense management.

Microsoft’s Azure Cognitive Services include the Computer Vision API and the Form Recognizer service, both of which use OCR technology. The Form Recognizer builds upon OCR by adding intelligent document understanding, enabling it to extract structured data from expense receipts automatically.

Other answer options are incorrect for the following reasons:

A. Semantic segmentation assigns labels to every pixel in an image, typically used in autonomous driving or medical imaging, not for text extraction.

B. Image classification identifies the overall category of an image (e.g., “This is a receipt”), but it does not extract the textual content.

C. Object detection identifies and locates objects in an image with bounding boxes but is not used for text reading or conversion.

Therefore, based on the official AI-900 training and Microsoft Learn content, the correct answer is D. Optical Character Recognition (OCR) — the technology that enables extracting textual information from scanned expense receipts.

The correct answer is A. Helping users understand the decisions made by an AI system.

According to the Microsoft Responsible AI principles described in the AI-900 study guide and Microsoft Learn Responsible AI documentation, transparency focuses on ensuring that users and stakeholders clearly understand how an AI system functions, makes decisions, and what data it relies on. This includes communicating limitations, assumptions, and levels of confidence in AI-driven outcomes.

For instance, if an AI model recommends loan approvals, transparency means explaining which factors influenced the decision and how much weight each factor carried. This helps build trust and accountability while allowing users to make informed judgments about the AI’s reliability.

Option review:

A. Helping users understand decisions made by an AI system — ✅ Correct.

B. Accountability: Refers to ensuring developers or organizations are responsible for AI outcomes.

C. Fairness: Ensures equal opportunities and mitigates bias.

D. Privacy and security: Focuses on protecting user data.

Hence, Transparency = clarity and explainability about AI processes.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify features of computer vision workloads”, Optical Character Recognition (OCR) is a core capability within the computer vision domain that enables systems to detect and extract text from images or documents. OCR technology can identify printed or handwritten characters in photographs, scanned documents, or camera feeds, and convert them into machine-readable text.

In this scenario, the task is to read the numbers on runners’ shirts in race photos. These numbers are textual or numeric characters embedded within images. OCR is specifically designed for this purpose — to locate and recognize characters within visual data and convert them into usable text. Once extracted, those numbers can be cross-referenced with a database to identify each runner.

Let’s analyze why the other options are incorrect:

A. Facial recognition focuses on identifying individuals based on unique facial features, not reading text or numbers.

C. Semantic segmentation classifies each pixel of an image into categories (for example, separating road, sky, and people), but it doesn’t read text.

D. Object detection identifies and locates objects within an image (such as detecting people or vehicles) but does not extract readable text or numbers.

Therefore, since the task involves reading textual or numeric content from an image, the appropriate type of computer vision to use is Optical Character Recognition (OCR).

According to the AI-900 Microsoft Learn modules on Conversational AI, a custom question answering solution built using Azure AI Language (formerly QnA Maker) enables a chatbot to respond to user questions based on a predefined knowledge base. When integrated with a bot, the solution can automatically respond to multiple user queries in real time without additional programming.

This capability is known as scalability and concurrency, which allows chatbots to manage simultaneous conversations with multiple users. This feature is built into the Azure Bot Service, meaning you don’t need to add extra “skills” or custom logic for concurrent interactions.

Other options require additional integration or logic:

Register customer complaints or purchases would require connecting the bot to a CRM or sales system.

Provide RMA numbers requires business process logic or database access.

Therefore, the out-of-the-box functionality of a custom question answering bot is the ability to answer questions from multiple users at once, which is supported natively by Azure Bot Service and the QnA knowledge base.

For evaluating a classification model, the appropriate metric from the options provided is the True Positive Rate (TPR), also known as Sensitivity or Recall. According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Evaluate model performance”, classification models are evaluated using metrics that measure how accurately the model predicts categorical outcomes such as “yes/no,” “spam/not spam,” or “approved/denied.”

The True Positive Rate measures the proportion of correctly identified positive cases out of all actual positive cases. Mathematically, it is expressed as:

True Positive Rate (Recall)=True PositivesTrue Positives + False Negatives\text{True Positive Rate (Recall)} = \frac{\text{True Positives}}{\text{True Positives + False Negatives}}True Positive Rate (Recall)=True Positives + False NegativesTrue Positives​

This metric is important when missing positive predictions carries a high cost, such as in medical diagnosis or fraud detection. Microsoft Learn highlights classification evaluation metrics such as accuracy, precision, recall, F1 score, and AUC (Area Under the Curve) as suitable for classification models.

The other options—Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²)—are regression metrics used to evaluate models that predict numeric values rather than categories. For example, they apply to predicting house prices or temperatures, not yes/no decisions.

Therefore, the correct classification evaluation metric among the choices is A. True Positive Rate.

According to the Microsoft Learn documentation and the Azure AI Fundamentals (AI-900) study guide, the GPT (Generative Pre-trained Transformer) family of models within Azure OpenAI Service is used for text-based natural language tasks, including summarization, content generation, and text completion.

When you need to summarize text from a document, GPT models (such as GPT-3.5 or GPT-4) can process large sections of text, extract the most relevant details, and generate concise summaries that retain the key meaning. The summarization task uses the model’s natural language understanding capabilities to identify core concepts and generate human-like, coherent text.

Other options are incorrect:

A. Whisper → Used for speech-to-text transcription, not text summarization.

B. DALL-E → Generates images from text prompts, not text summaries.

C. Codex → Specializes in code generation and completion, not document summarization.