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

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore Azure Machine Learning”, when you publish an inference pipeline (a deployed web service for real-time predictions) using Azure Machine Learning designer, you make the model accessible as a RESTful endpoint. Consumers—such as applications, scripts, or services—interact with this endpoint to submit data and receive predictions.

To securely access this deployed pipeline, two critical parameters are required:

REST endpoint (Option D):The REST endpoint is a URL automatically generated when the inference pipeline is deployed. It defines the network location where clients send HTTP POST requests containing input data (usually in JSON format). The endpoint routes these requests to the deployed model, which processes the data and returns prediction results. The REST endpoint acts as the primary access point for consuming the model’s inferencing capability programmatically.

Authentication key (Option C):The authentication key (or API key) is a security token provided by Azure to ensure that only authorized users or systems can access the endpoint. When invoking the REST service, the key must be included in the request header (typically as the value of the Authorization header). This mechanism enforces secure, authenticated access to the deployed model.

The other options are incorrect:

A. The model name is not required to consume the endpoint; it is used internally within the workspace.

B. The training endpoint is used for training pipelines, not for inference.

Therefore, according to Microsoft’s official AI-900 learning objectives and Azure Machine Learning documentation, when consuming a published inference pipeline, you must use both the REST endpoint (D) and the authentication key (C). These parameters ensure secure, controlled, and programmatic access to the deployed AI model for real-time predictions.

In machine learning, especially within the Microsoft Azure AI Fundamentals (AI-900) framework, datasets used for supervised learning are composed of features (inputs) and labels (outputs). According to the Microsoft Learn module “Explore the machine learning process”, a feature is any measurable property or attribute used by the model to make predictions, whereas a label is the actual value or category the model is trying to predict.

Household Income → FeatureA feature (also known as an independent variable) represents the input data that the machine learning algorithm uses to detect patterns or correlations. In this dataset, Household Income is a numeric value that influences the prediction of house price categories. During training, the model learns how variations in household income correlate with changes in the house price category. Microsoft Learn defines features as “the attributes or measurable inputs that are used to train the model.” Thus, Household Income serves as a predictive input or feature.

House Price Category → LabelThe label (or dependent variable) represents the output the model aims to predict. It is the known result during training that helps the algorithm learn correct mappings between features and outcomes. In this scenario, House Price Category—which can take values such as “Low,” “Middle,” or “High”—is the classification outcome that the model will predict based on household income (and possibly other variables). According to Microsoft Learn, “the label is the variable that contains the known values that the model is trained to predict.”

In summary, the dataset defines a supervised learning classification problem, where Household Income is the feature (input) and House Price Category is the label (output) that the model will learn to predict.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn documentation for Azure AI Custom Vision, this service is a specialized part of the Azure AI Vision family that enables developers to train custom image classification and object detection models. It allows organizations to build tailored computer vision models that recognize images or specific objects relevant to their business needs.

Detect objects in an image → YesThe Azure AI Custom Vision service supports both image classification (assigning an image to one or more categories) and object detection (identifying and locating objects within an image using bounding boxes). This means it can indeed detect and differentiate multiple objects in a single image, making this statement true.

Requires your own data to train the model → YesThe Custom Vision service is designed to be customizable. Unlike prebuilt Azure AI Vision models that work out of the box, Custom Vision requires you to upload and label your own dataset for training. The model then learns from your examples to perform specialized image recognition tasks relevant to your domain. Thus, this statement is also true.

Analyze video files → NoWhile Custom Vision can analyze images, it does not directly process or analyze video files. Video analysis is handled by a different service—Azure Video Indexer—which can extract insights such as spoken words, scenes, and faces from videos.

In summary:

✅ Yes – Detect objects in images

✅ Yes – Requires your own data

❌ No – Does not analyze video files.

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.

The correct answer is “An embedding.”

In the context of large language models (LLMs) such as GPT-3, GPT-3.5, or GPT-4, an embedding refers to a multi-dimensional numeric vector representation assigned to each word, token, or phrase. According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation for Azure OpenAI embeddings, embeddings are used to represent textual or semantic meaning in a numerical form that a machine learning model can process mathematically.

Each embedding captures the semantic relationships between words. Words or tokens with similar meanings (for example, “car” and “automobile”) are represented by vectors that are close together in the multi-dimensional space, while unrelated words (like “tree” and “laptop”) are farther apart. This vector representation enables the model to understand context, similarity, and relationships between different pieces of text.

Embeddings are fundamental in tasks such as:

Semantic search: Finding documents or sentences with similar meaning.

Clustering: Grouping related concepts together.

Recommendation systems: Suggesting similar content based on text meaning.

Contextual understanding: Helping generative models produce coherent and context-aware text.

Option review:

Attention: A mechanism used within transformers to focus on relevant parts of input sequences but not a representation of words.

A completion: Refers to the generated text output from a model, not the internal representation.

A transformer: The architecture that powers models like GPT, not the vector representation of tokens.

Therefore, the correct term for a multi-dimensional vector assigned to each word or token in a large language model (LLM) is An embedding, which represents how meaning is numerically encoded and compared within language models.

The Azure AI Language service (part of Azure Cognitive Services) provides a set of natural language processing (NLP) capabilities designed to analyze and interpret text data. Its core features include language detection, key phrase extraction, sentiment analysis, and named entity recognition (NER).

Language Identification – YESAccording to the Microsoft Learn module “Analyze text with Azure AI Language,” one of the service’s built-in capabilities is language detection, which determines the language of a given text string (e.g., English, Spanish, or French). This allows applications to automatically adapt to multilingual input.

Handwritten Signature Detection – NOThe Azure AI Language service only processes text-based data; it does not analyze images or handwriting. Detecting handwritten signatures requires computer vision capabilities, specifically Azure AI Vision or Azure AI Document Intelligence, which can extract and interpret visual content from scanned documents or images.

Identifying Companies and Organizations – YESThe Named Entity Recognition (NER) feature within Azure AI Language can identify entities such as people, locations, dates, organizations, and companies mentioned in text. It tags these entities with categories, enabling structured analysis of unstructured data.

✅ Summary:

Language detection → Yes (supported by AI Language).

Handwritten signatures → No (requires Computer Vision).

Entity recognition for companies/organizations → Yes (supported by AI Language NER).

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify features of common machine learning types”, there are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Within supervised learning, two common approaches are regression and classification, while clustering is a primary example of unsupervised learning.

“You train a regression model by using unlabeled data.” – No.Regression models are trained with labeled data, meaning the input data includes both features (independent variables) and target labels (dependent variables) representing continuous numerical values. Examples include predicting house prices or sales forecasts. Unlabeled data (data without target output values) cannot be used to train regression models; such data is used in unsupervised learning tasks like clustering.

“The classification technique is used to predict sequential numerical data over time.” – No.Classification is used for categorical predictions, where outputs belong to discrete classes, such as spam/not spam or disease present/absent. Predicting sequential numerical data over time refers to time series forecasting, which is typically a regression or forecasting problem, not classification. The AI-900 syllabus clearly separates classification (categorical prediction) from regression (continuous value prediction) and time series (temporal pattern analysis).

“Grouping items by their common characteristics is an example of clustering.” – Yes.This statement is correct. Clustering is an unsupervised learning technique used to group similar data points based on their features. The AI-900 study materials describe clustering as the process of “discovering natural groupings in data without predefined labels.” Common examples include customer segmentation or document grouping.

Therefore, based on Microsoft’s AI-900 training objectives and definitions:

Regression → supervised learning using labeled continuous data (No)

Classification → categorical prediction, not sequential numeric forecasting (No)

Clustering → grouping by similarity (Yes)

According to the Microsoft Azure AI Fundamentals (AI-900) study guide and official Microsoft Learn modules under “Describe features of common AI workloads”, Conversational AI refers to technology that enables computers to engage in dialogue or conversation with users through natural language, whether by text or speech. The interactive answering of user-entered questions through a chat interface or virtual assistant is a direct example of a conversational AI workload.

Microsoft defines Conversational AI as systems that use natural language processing (NLP) and language understanding models to interpret what users are asking and respond appropriately. This includes chatbots, virtual assistants (like Cortana or Azure Bot Service), and automated customer service systems that simulate a human-like conversation. In this case, when an application answers questions that a user types interactively, the AI model is processing human language inputs, deriving intent, and generating meaningful replies — precisely what conversational AI is designed to do.

By contrast:

Anomaly detection identifies unusual patterns in data, typically used for fraud detection or equipment monitoring — not interactive dialogue.

Computer vision deals with interpreting images or video (e.g., object detection, facial recognition), unrelated to answering text-based questions.

Forecasting uses historical data to predict future trends or outcomes, often in sales or demand prediction scenarios.

The AI-900 guide emphasizes that Conversational AI helps businesses improve customer interaction efficiency by offering instant, automated, and consistent responses. It enables real-time engagement 24/7 and integrates with tools such as Azure Bot Service, Azure Cognitive Service for Language, and QnA Maker (now part of Azure AI Language Service).

Therefore, based on the Microsoft Learn objectives and definitions from the official AI-900 curriculum, the interactive answering of user questions in an application is best categorized as Conversational AI.

The correct mappings are based on the Microsoft Azure AI Fundamentals (AI-900) curriculum topic: “Describe features of computer vision workloads.” Microsoft divides computer vision tasks into key workload types — image classification, object detection, facial recognition, and optical character recognition (OCR) — each designed for specific visual analysis objectives.

Identify celebrities in images → Facial recognitionFacial recognition goes beyond simple face detection; it can identify or verify specific individuals by comparing facial features with known profiles. According to Microsoft Learn, the Face service in Azure Cognitive Services can detect, recognize, and identify people in photos or videos. Recognizing celebrities or known individuals is a prime example of facial recognition.

Extract movie title names from movie poster images → Optical Character Recognition (OCR)OCR is used to detect and extract text content from images, such as printed or handwritten words. Azure’s Computer Vision API uses OCR technology to read text in various languages from photos, scanned documents, or posters. Therefore, extracting movie titles or actor names from a poster image is a perfect use case for OCR.

Locate vehicles in images → Object detectionObject detection identifies and locates specific objects within an image, returning bounding boxes that indicate their positions. In Azure, the Custom Vision service or Computer Vision object detection models are used to detect multiple objects like vehicles, pedestrians, or animals in a single image.

Summary:

Facial recognition → Identifies specific people (celebrities)

OCR → Extracts text (movie titles)

Object detection → Finds and locates physical items (vehicles)

Thus, the verified and official answer is:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify Azure services for conversational AI,” the Azure AI Bot Service is specifically designed to create intelligent conversational agents (chatbots) that can interact with users across multiple communication channels, such as web chat, social media, phone calls, Microsoft Teams, and digital assistants.

In this scenario, customers need the ability to query the status of their orders through various interfaces — including voice and text platforms. Azure AI Bot Service enables this by integrating with Azure AI Language (for understanding natural language), Azure Speech (for speech-to-text and text-to-speech capabilities), and Azure Communication Services (for telephony or chat integration).

The bot can interpret user input like “Where is my order?” or “Check my delivery status,” call backend systems (such as an order database or API), and then respond appropriately to the user through the same communication channel.

Let’s analyze the incorrect options:

B. Azure AI Translator Service: Used for real-time text translation between languages; it doesn’t handle conversation logic or database queries.

C. Azure AI Document Intelligence model: Extracts data from structured and semi-structured documents (e.g., invoices, receipts), not user queries.

D. Azure Machine Learning model: Builds and deploys predictive models, but doesn’t provide conversational or multi-channel interaction capabilities.

Thus, for enabling multi-channel conversational experiences where customers can inquire about order statuses using voice, chat, or digital assistants, the most appropriate solution is Azure AI Bot Service, as outlined in Azure’s AI conversational workload documentation.

B: Azure ' s Computer Vision service provides developers with access to advanced algorithms that process images and return information based on the visual features you ' re interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.

C: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents.

Azure Bot Service and Azure Cognitive Services can be integrated. → Yes

Azure Bot Service engages with customers in a conversational manner. → Yes

Azure Bot Service can import frequently asked questions (FAQ) to question and answer sets. → Yes

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All three statements are true, as confirmed by the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Explore conversational AI.” The Azure Bot Service is Microsoft’s platform for building, deploying, and managing intelligent bots that can communicate naturally with users across various channels (web, Teams, Facebook Messenger, etc.).

Azure Bot Service and Azure Cognitive Services can be integrated → YesMicrosoft Learn specifies that Azure Bot Service can be enhanced with Azure Cognitive Services such as Language Understanding (LUIS), QnA Maker, and Speech Services to add intelligence. For example, integration with LUIS allows bots to understand user intent and context, while QnA Maker helps them respond accurately to FAQs. As stated in the official documentation: “The Azure Bot Service can be combined with Cognitive Services to create bots that understand language, speech, and meaning.”

Azure Bot Service engages with customers in a conversational manner → YesThe primary function of Azure Bot Service is to create conversational AI agents that interact naturally with users. These bots simulate human-like dialogue using text or speech. According to Microsoft Learn, “Bots created using Azure Bot Service communicate with users in a conversational format through natural language.”

Azure Bot Service can import frequently asked questions (FAQ) to question and answer sets → YesAzure Bot Service can integrate with the QnA Maker (now part of Azure Cognitive Service for Language) to automatically import FAQs from existing documents or web pages and generate a knowledge base of question-answer pairs. This allows the bot to respond intelligently to customer queries.

In conclusion, Azure Bot Service supports intelligent, conversational interaction, integrates seamlessly with Cognitive Services, and can use QnA Maker to import and manage FAQ-based knowledge sets—making all three statements true.

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore fundamental principles of machine learning,” a regression model is used when the goal is to predict a continuous numerical value based on historical data.

In this question, the task is to predict the sale price of auctioned items, which is a numeric output that can take on a wide range of values (for example, $50.25, $199.99, etc.). This makes it a regression problem because the output is continuous rather than categorical.

Regression models analyze the relationship between input features (such as item type, condition, age, bidding history, or demand) and a numerical target variable (the sale price). Common regression algorithms include linear regression, decision tree regression, and neural network regression. In Azure Machine Learning, these models are trained using labeled datasets containing known outcomes to learn patterns and make future predictions.

Let’s review the incorrect options:

Classification: Used to predict discrete categories or labels, such as “sold” vs. “unsold” or “low,” “medium,” “high.” It cannot output continuous numeric predictions.

Clustering: An unsupervised technique used to group similar data points based on shared characteristics, not to predict specific numeric outcomes.

Therefore, because predicting a sale price involves forecasting a continuous numerical value, the correct model type is Regression.

This aligns with Microsoft’s AI-900 teaching that regression is used for tasks such as:

Predicting house prices

Forecasting sales revenue

Estimating car values or auction prices

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.

When building a K-means clustering model, all features (variables) used in the model must be numeric in nature. According to the Microsoft Azure AI Fundamentals (AI-900) study materials and standard machine learning theory, K-means clustering is an unsupervised learning algorithm that groups data points into clusters based on their similarity — specifically by minimizing the Euclidean distance between data points and their assigned cluster centroids.

Because the K-means algorithm depends on distance calculations, it requires numeric data types. The Euclidean distance (or similar measures) can only be computed between numerical values. Therefore, all categorical or text data must first be converted into numeric form through feature engineering techniques such as one-hot encoding, label encoding, or embedding vectors, depending on the nature of the data.

Here’s how K-means works in summary:

The algorithm initializes a predefined number of centroids (K).

Each data point is assigned to the nearest centroid based on numeric distance.

The centroids are recalculated as the mean of the points in each cluster.

The process repeats until convergence.

If non-numeric data (e.g., text or Boolean) were provided, the model would not be able to calculate distances accurately, leading to computational errors.

Other options are incorrect:

Boolean and integer types can represent numeric values but are considered special cases; the algorithm requires general numeric representation (e.g., continuous values).

Text cannot be processed directly without conversion.

Thus, according to Azure Machine Learning and AI-900 official concepts, all features in a K-means clustering model must be numeric to ensure valid mathematical operations and clustering accuracy.

In Microsoft’s Responsible AI framework, the Reliability and Safety principle ensures that AI systems perform consistently, safely, and as intended across diverse conditions — even when faced with incomplete, unusual, or unexpected data. Correctly handling unusual or missing values in a dataset directly demonstrates this principle, as it helps prevent faulty predictions, biased results, or unsafe system behaviors.

According to the Microsoft Learn Responsible AI module (from the AI-900 and AI-102 study paths), a reliable AI model should maintain its performance when encountering data anomalies. This includes validating inputs, managing missing or extreme values, and testing models to ensure they behave as expected in real-world scenarios. Such practices make AI systems robust and trustworthy, which aligns exactly with the Reliability and Safety principle.

The other Responsible AI principles address different concerns:

Inclusiveness: Ensures AI empowers and serves all users equitably.

Privacy and Security: Focuses on safeguarding personal data and preventing unauthorized access.

Transparency: Ensures that AI decisions are understandable and explainable to users.

While all principles are essential, managing data integrity and system stability—including how a model responds to missing or anomalous values—is primarily a matter of reliability and safety. It ensures the AI behaves predictably and minimizes risks of errors or unintended harm.

Therefore, the correct completion of the sentence is:

“Correctly handling unusual or missing values is an example of the application of the Reliability and Safety principle for Responsible AI.”

The Azure AI Language service provides several NLP features, including language detection, key phrase extraction, sentiment analysis, and named entity recognition (NER).

When you need to extract specific data points such as dates, names, organizations, or locations from unstructured text (for example, social media posts), the correct feature is Entity Recognition.

Entity Recognition identifies and classifies information in text into predefined categories like:

Person names (e.g., “John Smith”)

Organizations (e.g., “Contoso Ltd.”)

Dates and times (e.g., “October 22, 2025”)

Locations, events, and quantities

This capability helps transform unstructured textual data into structured data that can be analyzed or stored.

Option analysis:

A (Language detection): Determines the language of a text (e.g., English, French).

B (Speech recognition): Converts spoken audio to text; not applicable here.

C (Key phrase extraction): Identifies important phrases or topics but not specific entities like names or dates.

D (Entity recognition): Correctly extracts names, dates, and other specific data from text.

Hence, the accurate feature for this scenario is D. Entity Recognition.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Describe features of natural language processing (NLP) workloads on Azure,” sentiment analysis is an NLP workload that determines the emotional tone or opinion expressed in a piece of text. This could be positive, negative, or neutral sentiment.

When an app analyzes social media posts to identify their tone, it is performing sentiment analysis, since it aims to understand the emotional context behind user-generated text such as tweets, reviews, or comments. Azure provides this functionality through the Azure Cognitive Services – Text Analytics API, which evaluates text and returns sentiment scores.

Other options are not suitable:

Key phrase extraction identifies main ideas in text but not tone.

Entity recognition identifies names of people, organizations, or locations.

Speech recognition converts spoken words into text, not emotional analysis.

Therefore, analyzing social media tone is an example of sentiment analysis, a key NLP workload in Microsoft’s AI-900 syllabus.