Free Microsoft AI-900 Practice Exam with Questions & Answers | Set: 6

To reduce operator load with a chatbot for predefined answers:

QnA Maker provides the knowledge base for answering questions automatically.

Azure Bot Service hosts and manages the chatbot interface to interact with users.Text Analytics and Translator Text are not required for basic Q & A chatbots.

 “Generative AI enables software applications to generate new content, such as language dialogs and images.” — YES

This statement is true. According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation, Generative AI refers to systems capable of creating new content such as text, audio, images, video, and code. Models like GPT, DALL·E, and Codex use deep learning to generate human-like responses, natural conversations, or creative media. This is a key differentiator between generative and discriminative AI — generative AI produces new data, while discriminative AI categorizes or analyzes existing data.

 “The difference between a large language model (LLM) and a small language model (SLM) is the number of variables in the model.” — YES

This statement is true. The primary distinction between an LLM and an SLM lies in the scale of parameters (variables) within the neural network. LLMs contain billions or even trillions of parameters, which enable them to capture complex linguistic patterns and perform broader tasks. SLMs have fewer parameters, making them faster but less capable of handling complex, context-rich tasks.

 “Generative AI is a type of supervised learning.” — NO

This statement is false. Generative AI models are typically trained using unsupervised or self-supervised learning methods. They learn by predicting missing or next elements in large text or image datasets rather than relying on labeled input-output pairs, which are used in supervised learning.

Microsoft’s Responsible AI Principles, as outlined in the AI-900 certification materials and official Microsoft documentation, emphasize six guiding principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. The principle of transparency means that AI systems should be designed so their decisions and processes are understandable and explainable to users and stakeholders.

In this scenario, the AI system is being developed to decide whether a loan should be approved. Such a decision directly affects people’s lives and finances, so it is essential that the system can explain which factors influenced its decision—for example, credit score, income, or payment history. Microsoft’s Responsible AI framework stresses that transparency helps ensure trust between humans and AI systems. When decisions are explainable, users can understand and contest the reasoning if necessary.

The other options do not align precisely with this scenario:

B. Inclusiveness focuses on making AI accessible to all people, regardless of ability or background.

C. Fairness ensures that AI systems treat all individuals equally and do not discriminate. While fairness is important for loan assessment, the question specifically highlights the need for explainability, not equality.

D. Privacy and Security deals with safeguarding user data, which is separate from explaining decisions.

Therefore, the principle demonstrated here is transparency, as it ensures decision-making processes are clear, explainable, and traceable—directly aligning with Microsoft’s responsible AI guidance.

Box 1: 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.

Box 2: accountability

Box 3: Privacy and security

As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used

https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Identify guiding principles for responsible AI”, responsible AI is built upon six foundational principles: Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability. Each principle serves to guide the ethical design, deployment, and management of artificial intelligence systems.

Fairness – This principle ensures that AI systems treat all people fairly and do not discriminate based on personal attributes such as gender, race, or age. The Microsoft Learn content emphasizes that “AI systems should treat everyone fairly” and that organizations must evaluate datasets and model outputs for bias. In this scenario, “The system must not discriminate based on gender, race” clearly aligns with Fairness because it directly addresses equitable treatment and unbiased decision-making.

Privacy and Security – Microsoft’s responsible AI framework stresses that “AI systems must be secure and respect privacy.” This means personal data should be safeguarded, processed lawfully, and visible only to authorized users. The statement “Personal data must be visible only to approved users” reflects the importance of protecting sensitive information and controlling access—precisely the intent of the Privacy and Security principle.

Transparency – Transparency refers to ensuring that users understand how AI systems operate and make decisions. Microsoft notes that “AI systems should be understandable and users should be able to know why decisions are made.” The requirement “Automated decision-making processes must be recorded so that approved users can identify why a decision was made” directly supports this principle. Transparency promotes trust and accountability by documenting the reasoning behind AI outputs.

Reliability and Safety, though another core principle, does not directly relate to any of the provided statements in this question.

This question tests the understanding of features and labels in machine learning, a core concept covered in the Microsoft Azure AI Fundamentals (AI-900) syllabus under “Describe fundamental principles of machine learning on Azure.”

In supervised machine learning, data is divided into features (inputs) and labels (outputs).

Features are the independent variables — measurable properties or characteristics used by the model to make predictions.

Labels are the dependent variables — the target outcome the model is trained to predict.

From the provided dataset, the goal of the Azure Machine Learning model is to predict product quality (Pass or Fail). Therefore:

Mass (kg) is a feature – Yes“Mass (kg)” represents an input variable used by the model to learn patterns that influence product quality. It helps the algorithm understand how variations in mass might correlate with passing or failing the quality test. Thus, it is correctly classified as a feature.

Quality Test is a label – YesThe “Quality Test” column indicates the outcome of the manufacturing process, marked as either Pass or Fail. This is the target the model tries to predict during training. In Azure ML terminology, this column is the label, as it represents the dependent variable.

Temperature (C) is a label – No“Temperature (C)” is an input that helps the model determine quality outcomes, not the outcome itself. It influences the quality result but is not the value being predicted. Therefore, temperature is another feature, not a label.

In conclusion, per Microsoft Learn and AI-900 study materials, features are measurable inputs (like mass and temperature), while the label is the target output (like the quality test result).

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify features of Computer Vision workloads on Azure”, Object Detection is a specific computer vision capability used to identify and locate multiple types of objects within a single image. Unlike image classification, which assigns one label to an entire image, object detection identifies individual objects, their categories, and their positions using bounding boxes or polygons.

In practical terms, Object Detection combines two key outputs:

Classification – recognizing what the object is (for example, “car”, “person”, “dog”).

Localization – determining where the object appears in the image by drawing bounding boxes around it.

This technology is commonly used in scenarios such as traffic monitoring (detecting vehicles and pedestrians), retail shelf analysis (detecting products and inventory levels), and manufacturing quality control (identifying defective parts).

Microsoft’s Azure Cognitive Services – Custom Vision includes a dedicated Object Detection domain, which allows developers to train custom models to recognize multiple object types within a single image. The service uses deep learning techniques, particularly convolutional neural networks (CNNs), to process pixel patterns and spatial relationships for accurate detection.

For contrast:

Image Classification identifies only the overall category of an image (e.g., “This is a cat”).

Image Description generates captions summarizing the visual content (e.g., “A cat sitting on a couch”).

Optical Character Recognition (OCR) detects and extracts text from images, not physical objects.

Therefore, per the official AI-900 learning content and Azure documentation, when the goal is to identify multiple types of items within a single image, the correct AI workload is Object Detection.

This question examines your understanding of Natural Language Processing (NLP) as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Explore natural language processing.” NLP is a branch of artificial intelligence that enables computers to analyze, understand, and generate human language — both written and spoken. Typical NLP tasks include text analytics, language understanding, sentiment analysis, key phrase extraction, and profanity detection.

Monitoring online service reviews for profanities → YesThis is a classic example of NLP. Detecting profane or inappropriate words in customer reviews requires analyzing text content. Azure Cognitive Services offers Content Moderator and Text Analytics APIs that can detect and filter profanity, sentiment, and offensive language automatically. Microsoft Learn states: “Natural language processing is used to process and analyze text to detect sentiment, key phrases, and inappropriate content.” Hence, this task is correctly classified as NLP.

Identifying brand logos in an image → NoThis task belongs to Computer Vision, not NLP. The Computer Vision API and Custom Vision service in Azure are designed to detect and classify visual elements like logos, objects, or scenes. Since it involves images, not text, it is unrelated to natural language processing.

Monitoring public news sites for negative mentions of a product → YesThis is another valid example of NLP. The process involves analyzing the sentiment of text from online articles to determine whether mentions of a product are positive, neutral, or negative. Azure Text Analytics provides prebuilt sentiment analysis and entity recognition capabilities that help automate such monitoring.

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.

The correct answers are B. Import chit-chat content from a predefined data source, C. Manually enter the questions and answers, and E. Generate the questions and answers from an existing webpage.

According to Microsoft Learn and the Azure AI Fundamentals (AI-900) study guide, the Question Answering feature of the Azure AI Language Service (formerly part of QnA Maker) allows developers to create a knowledge base (KB) that enables a chatbot to answer common questions automatically. This knowledge base can be built in three main ways:

Import chit-chat content (B):Azure provides predefined chit-chat datasets that can be imported to make a bot more conversational and natural. This includes small talk such as greetings, acknowledgments, and polite responses (for example, “How are you?” → “I’m doing great, thanks!”). Importing this content enriches the bot’s personality and improves user engagement.

Manually enter questions and answers (C):Developers can manually add pairs of questions and answers directly into the question answering knowledge base. This approach is suitable for custom FAQs or domain-specific content. It gives complete control over how each question is phrased and what answer is returned, ensuring high precision and clarity.

Generate questions and answers from an existing webpage (E):Azure AI Language can automatically extract Q & A pairs from a website’s FAQ or support page. This is done by providing the webpage URL to the service, which scans the page and builds a knowledge base from the detected questions and corresponding answers.

The other options are incorrect:

A (Cortana channel) relates to bot deployment, not knowledge creation.

D (Automated ML) is used for predictive modeling, not for building Q & A datasets.

Thus, the verified correct answers are B, C, and E.