Pass Using AI-900 Exam Dumps

QnA Maker is an Azure Cognitive Service used to build question-and-answer knowledge bases from structured and unstructured documents, such as FAQs, product manuals, or webpages. According to the AI-900 study guide and Microsoft Learn module “Build a knowledge base with QnA Maker”, this service allows you to extract question-answer pairs from existing data sources like FAQ pages, PDF files, or Word documents.

In this scenario, you have:

A product troubleshooting guide (Word document)

A FAQ webpage

QnA Maker can automatically read both sources, extract relevant Q & A pairs, and create a knowledge base that your chatbot can use to respond to user queries intelligently.

To clarify the other options:

A. Azure Bot Service provides the chatbot interface and conversation logic but doesn’t extract knowledge from documents.

B. Language Understanding (LUIS) identifies intents and entities in natural language input, but it’s not used to read document content.

C. Text Analytics is used for key phrase extraction and sentiment analysis, not Q & A creation.

Therefore, the correct service for processing FAQ-style and document-based content into a question-answering bot is QnA Maker.

According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module “Describe features of common AI workloads”, a chatbot (also known as a conversational AI agent) is a solution designed to interact with users through natural language conversation across multiple channels such as email, phone, webchat, and messaging apps.

Chatbots use Natural Language Processing (NLP) to interpret what users are saying, identify their intent, and provide relevant responses. In Azure, this functionality is implemented using the Azure Bot Service integrated with the Azure Cognitive Service for Language (Question Answering and Language Understanding). The study guide emphasizes that chatbots are used in customer service, information retrieval, and support automation to reduce the workload on human agents and improve response times.

The requirement in this question — supporting email, phone, and live chat channels — aligns exactly with the definition of a conversational AI chatbot, which can operate across multiple communication platforms. Microsoft Learn clearly identifies that chatbots can be deployed to assist customers in retrieving information, answering FAQs, and escalating complex issues when necessary.

The other options are incorrect because:

A. NLP is the underlying technology used by the chatbot but not the solution itself.

B. Computer vision involves analyzing images or videos, which is unrelated to this scenario.

C. Machine learning is a broader AI field and not a specific customer support solution type.

 “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.

The question describes a process where an AI system generates text that describes an image — for example, “A dog playing with a ball in the park.” This process is an example of image classification, a core workload in computer vision that allows a system to recognize and categorize the content of an image.

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module “Identify Azure services for computer vision,” image classification involves analyzing the pixels of an image and assigning one or more predefined categories or labels to it. In more advanced implementations, image classification models are combined with caption generation algorithms to produce descriptive text. For example, Azure AI Vision can generate captions and tags that describe an image’s content, such as “outdoor scene,” “a person riding a bicycle,” or “a group of people smiling.”

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

Facial detection: Identifies the presence and location of human faces in an image, but does not generate descriptive text.

Object detection: Identifies and locates multiple objects within an image by drawing bounding boxes, not by describing the overall scene.

Optical character recognition (OCR): Extracts text from images or scanned documents (for example, reading a street sign), but it doesn’t create descriptive language about what’s depicted.

Therefore, the correct answer is Image classification, as it aligns with the AI-900 learning objective that describes this task as recognizing and categorizing the main content of an image, often leading to caption generation in modern vision models such as those in Azure AI Vision.