Microsoft Azure AI Fundamentals AI-900 Exam Questions with Experts Answers Updated Recently

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module “Explore natural language processing (NLP) in Azure”, Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. NLP is used to extract meaning and intent from text or speech, perform sentiment analysis, identify entities, and classify content based on context.

One of the primary applications of NLP is text classification, where an AI model automatically categorizes text documents or messages into predefined classes. Classifying emails as work-related or personal is a textbook example of this NLP capability. It involves analyzing the words, phrases, and structure of the text to determine the email’s category. Microsoft Learn highlights this type of problem as document classification, an essential NLP use case often implemented through Azure Cognitive Services such as Text Analytics or Language Studio.

Let’s examine why the other options are incorrect:

Predict the number of future car rentals – This is a time series forecasting or regression task, not NLP.

Predict which website visitors will make a transaction – This is a predictive analytics or machine learning classification problem based on behavioral data, not language understanding.

Stop a process in a factory when extremely high temperatures are registered – This relates to IoT automation or sensor-based anomaly detection, not NLP.

Therefore, based on Microsoft’s AI-900 materials, Natural Language Processing is best used for tasks involving understanding and classifying text, such as classifying email messages as work-related or personal. This example perfectly aligns with NLP’s goal—to enable machines to process and derive insights from human language inputs.

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.

The correct answer is Azure AI Language, which includes the Question Answering capability (previously known as QnA Maker). According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation, the Azure AI Language service can be used to create a knowledge base from frequently asked questions (FAQ) and other structured or semi-structured text sources.

This service allows developers to build intelligent applications that can understand and respond to user questions in natural language by referencing prebuilt or custom knowledge bases. The Question Answering feature extracts pairs of questions and answers from documents, websites, or manually entered data and uses them to construct a searchable knowledge base. This knowledge base can then be integrated with Azure Bot Service or other conversational platforms to create interactive, self-service chatbots.

Here’s how it works:

Developers upload FAQ documents, URLs, or structured content.

Azure AI Language processes the content and identifies logical question-answer pairs.

The model stores these pairs in a knowledge base that can be queried by user input.

When users ask questions, the model finds the best matching answer using natural language understanding techniques.

In contrast:

Azure AI Document Intelligence (Form Recognizer) is used to extract structured data from forms and documents, not to create FAQ knowledge bases.

Azure AI Bot Service is for managing and deploying conversational bots but does not generate knowledge bases.

Microsoft Bot Framework SDK provides tools for building conversational logic but still requires a knowledge source like Question Answering from Azure AI Language.

Therefore, the service that can create a knowledge base from FAQ content is Azure AI Language.

This question evaluates your understanding of how different Azure AI workloads correspond to specific tasks in image, text, and content generation scenarios, as explained in the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn modules covering common AI workloads and Azure services.

Generate a caption from a given image → Computer VisionThis is a computer vision task because it involves analyzing the visual elements of an image and producing descriptive text (a caption). Azure AI Vision provides image analysis and captioning capabilities through its Describe Image API, which uses deep learning models to recognize objects, scenes, and actions in an image and automatically generate natural-language descriptions (e.g., “A cat sitting on a sofa”).

Generate an image from a given caption → Generative AIThis task belongs to Generative AI, which focuses on creating new content such as text, code, or images based on prompts. Tools like Azure OpenAI Service with DALL-E can interpret text descriptions and generate realistic images that match the given caption. Generative AI is capable of creative synthesis, not just analysis, making it the appropriate category.

Generate a 200-word summary from a 2,000-word article → Text AnalyticsText analytics (a subset of natural language processing) allows summarization, sentiment analysis, and entity recognition from large text corpora. Azure AI Language includes text summarization capabilities that condense long documents into concise summaries while preserving meaning and key information.