Free Microsoft AI-900 Practice Exam with Questions & Answers

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 Hence, while GPT-based models can produce highly coherent text, they may sometimes generate inaccurate, outdated, or fabricated information (commonly referred to as “hallucinations”).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.

Question number: 1

Answer: B

Full Detailed Explanation with exact Extract from your Official Study guide and Trained Data at least 250 to 300 words in Explanation:

The correct answer is B. a reduced workload for the customer service agents.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Describe features of common AI workloads”, conversational AI solutions such as chatbots are primarily designed to automate repetitive and routine customer interactions. The key business value emphasized in these materials is operational efficiency—chatbots allow organizations to respond to a high volume of customer queries without relying solely on human agents. This results in reduced workload, lower operational costs, and faster response times.

Microsoft’s AI-900 learning objectives highlight that AI can be applied to automate tasks that previously required human interaction. In the context of customer support, a webchat bot powered by Azure AI services (such as Azure Bot Service or Azure Cognitive Services for Language) can handle frequently asked questions like order status, password resets, or basic troubleshooting. This allows human agents to focus their time and skills on more complex issues that require empathy, reasoning, or decision-making—tasks that AI cannot yet handle as effectively.

Additionally, the AI-900 course materials explain that one of the measurable business benefits of deploying AI-driven chatbots is improved efficiency and scalability. Chatbots can handle thousands of simultaneous interactions, something that human teams cannot easily do. As a result, the organization experiences reduced operational pressure on support staff, improved customer satisfaction due to quicker responses, and optimized resource utilization.

Options A and C are incorrect because chatbots do not directly influence sales growth or product reliability. While increased customer satisfaction might indirectly support sales, it is not the primary or guaranteed outcome of implementing a chatbot. Similarly, product reliability is tied to engineering quality, not customer service automation.

Therefore, based on the official AI-900 study materials and Microsoft Learn concepts, the best and verified answer is B. a reduced workload for the customer service agents.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify features of conversational AI workloads on Azure”, a Language Understanding (LUIS) model is designed to interpret natural language input by identifying intents (the purpose of an utterance) and entities (specific data items in the utterance).

Every LUIS model automatically includes a special intent called “None.” This intent is used to handle utterances that do not fall into any of the model’s defined intents. Adding examples of irrelevant or out-of-scope utterances to the None intent helps the model learn to recognize when a user’s input does not match any existing categories.

For example, if your e-commerce chatbot handles intents such as “TrackOrder” and “CancelOrder,” but a user says “What’s your favorite color?”, that input should be mapped to the None intent so the bot can respond appropriately, such as “I’m not sure how to answer that.”

The AI-900 curriculum emphasizes that including diverse None intent examples improves model robustness and prevents false matches, thereby enhancing user experience.

Other options are incorrect:

A. Test the model by using new utterances: Testing is important but does not define how to detect out-of-scope inputs.

C. Create a prebuilt task entity: Entities extract specific data but are unrelated to intent classification.

D. Create a new model: Unnecessary; handling out-of-scope utterances is done within the same model via the None intent.

✅ Final Answer: B. Add utterances to the None intent

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.

The correct matches are Custom Vision, Form Recognizer, and Face — each corresponding to a distinct capability under Azure Cognitive Services as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules on Computer Vision workloads.

Custom Vision → Identify objects in an imageThe Custom Vision service is part of the Azure Cognitive Services suite that enables developers to train custom image classification and object detection models. Unlike the prebuilt Computer Vision API, Custom Vision allows users to upload their own labeled images and teach the model to recognize specific objects relevant to their business context. The AI-900 syllabus explains that Custom Vision is ideal for tasks such as identifying products on a shelf, categorizing images, or detecting defects in manufacturing.

Form Recognizer → Automatically import data from an invoice to a databaseForm Recognizer is a document processing AI service that extracts structured data from forms, receipts, and invoices. It uses optical character recognition (OCR) combined with layout and key-value pair detection to automatically capture information such as invoice numbers, amounts, and vendor names. The AI-900 study materials highlight this service under the Document Intelligence category, emphasizing its ability to streamline data entry and business automation workflows by importing extracted data directly into databases or applications.

Face → Identify people in an imageThe Face service provides advanced facial detection and recognition capabilities. It can locate faces in images, compare similarities between faces, identify known individuals, and even detect facial attributes such as age or emotion. The AI-900 course classifies this under Computer Vision services for person identification and security-related use cases such as access control or identity verification.

Thus, each mapping aligns precisely with the AI-900 official learning outcomes on Cognitive Services capabilities:

Custom Vision → Object recognition

Form Recognizer → Data extraction from forms

Face → People identification

✅ Final verified configuration:

Custom Vision → Identify objects in an image

Form Recognizer → Automatically import data from an invoice to a database

Face → Identify people in an image

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore computer vision in Microsoft Azure,” computer vision is a field of artificial intelligence that enables computers to interpret and understand visual information from the world — such as images or videos.

In this scenario, the task is to count the number of animals in an area based on a video feed. This requires the system to:

Detect the presence of animals in each frame of the video (object detection).

Track and count them across multiple frames as they move.

These are classic computer vision tasks, as they involve analyzing visual inputs (video or image data) and identifying objects (in this case, animals). Azure provides services such as Azure Computer Vision, Custom Vision, and Video Indexer, which can perform object detection, counting, and activity recognition using AI models trained on visual datasets.

Why the other options are incorrect:

Forecasting: Involves predicting future values based on historical data (e.g., predicting sales or weather), not analyzing video feeds.

Knowledge mining: Focuses on extracting insights from large text-based document repositories, not images or videos.

Anomaly detection: Identifies unusual patterns in numeric or time-series data, not visual objects.

Therefore, identifying and counting animals in video footage falls under computer vision, since it uses AI to visually detect, classify, and quantify objects in real-time or recorded feeds.

Reliability and safety: To build trust, it ' s critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.

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.

According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Azure Cognitive Services documentation, the Custom Vision service is a specialized computer vision tool that allows users to build, train, and deploy custom image classification and object detection models. It is part of the Azure Cognitive Services suite, designed for scenarios where pre-built Computer Vision models do not meet specific business requirements.

“The Custom Vision service can be used to detect objects in an image.” → YesThis statement is true. The Custom Vision service supports object detection, enabling the model to identify and locate multiple objects within a single image using bounding boxes. For example, it can locate cars, products, or animals in photos.

“The Custom Vision service requires that you provide your own data to train the model.” → YesThis statement is true. Unlike pre-trained models such as the standard Computer Vision API, the Custom Vision service requires users to upload and label their own images. The system uses this labeled dataset to train a model specific to the user’s scenario, improving accuracy for custom use cases.

“The Custom Vision service can be used to analyze video files.” → NoThis statement is false. The Custom Vision service works only with static images, not videos. To analyze video files, Azure provides Video Indexer and Azure Media Services, which are designed for extracting insights from moving visual content.

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).