Microsoft Updated AI-900 Exam Questions and Answers by aida

To generate cartoons or images from text descriptions, the correct Azure OpenAI model is DALL-E. As described in Microsoft’s OpenAI integration documentation, DALL-E is a generative image model that converts natural language prompts into images, illustrations, and artwork.

Codex is for code generation, GPT-3.5 and GPT-4 are for text and reasoning tasks, not image creation. Therefore, B. DALL-E is correct.

The correct answer is A. Azure AI Document Intelligence (previously known as Form Recognizer). This Azure Cognitive Service is specifically designed to extract structured data and key information from scanned documents, forms, and contracts using advanced Optical Character Recognition (OCR) combined with machine learning models.

According to the Microsoft Learn module “Extract data from documents with Azure AI Document Intelligence”, this service enables automated data extraction from unstructured or semi-structured documents such as contracts, invoices, receipts, and purchase orders. It identifies key-value pairs, tables, and fields such as names, dates, amounts, and signatures. This makes it ideal for digitizing legal and business documents like contracts into structured formats that can be easily searched or stored in databases.

Azure AI Document Intelligence offers several model types:

Prebuilt models for common documents (invoices, receipts, business cards, etc.).

Custom models trained on your specific contract layouts.

Layout model for extracting raw text, tables, and structures.

The other options are incorrect:

B. Azure AI Immersive Reader enhances reading comprehension and accessibility but does not extract data from documents.

C. Azure OpenAI provides natural language generation and understanding but is not used for scanning or data extraction.

D. Azure AI Search indexes and searches textual or document content but relies on other services (like Document Intelligence) to extract the data first.

Therefore, to automatically extract details such as contract terms, names, dates, and signatures from scanned contract images, the best Microsoft AI service is A. Azure AI Document Intelligence

This question maps each Azure AI service to its correct capability based on the Microsoft Azure AI Fundamentals (AI-900) syllabus and Microsoft Learn documentation on Azure Cognitive Services.

Classify and label images → Azure AI VisionAzure AI Vision (formerly Computer Vision) provides capabilities to analyze visual content, detect objects, classify images, and extract information from pictures. It includes object detection, image classification, and tagging, which are core vision tasks. This service enables businesses to build solutions that understand visual input, such as identifying products, reading signs, or detecting faces in images.

Generate conversational responses → Azure OpenAI ServiceAzure OpenAI Service integrates powerful large language models such as GPT-3.5 and GPT-4, capable of generating human-like text responses, summarizations, translations, and dialogues. These models are designed for natural language generation (NLG) and conversational AI, making them ideal for chatbots, virtual agents, and intelligent assistants that produce dynamic, context-aware replies.

Convert speech to text in real time → Azure AI SpeechAzure AI Speech provides speech-to-text capabilities (speech recognition) that convert spoken language into written text instantly. It is commonly used in transcription services, voice command systems, and live captioning applications. Additionally, the Speech service supports text-to-speech (speech synthesis) and speech translation, making it versatile for voice-based AI applications.

By understanding each service’s specialization—Vision for visual data, OpenAI for generative text, and Speech for audio processing—you can correctly match the capabilities.

In unsupervised machine learning, the algorithm learns patterns or structure within data without pre-labeled outputs or target values. The primary goal is to discover hidden relationships or group similar data points automatically. The Microsoft Azure AI Fundamentals (AI-900) study materials identify clustering as the key example of unsupervised learning.

In clustering, algorithms such as K-means, hierarchical clustering, or DBSCAN group data based on feature similarity. For example, a business may cluster customers by purchase behavior to discover natural customer segments without prior category labels. The model finds inherent patterns within the data rather than being told what to predict.

By contrast, classification and regression are supervised learning techniques. In supervised learning, the algorithm is trained using labeled data where correct outputs are already known. Therefore, the correct answer is B. Clustering, as it best represents unsupervised learning in Azure AI-900 principles.