According to the Microsoft Azure AI Fundamentals (AI-900) syllabus and Microsoft Learn module "Describe features of natural language processing (NLP) workloads on Azure," translation is a core NLP workload that converts text from one language into another while maintaining meaning and context.
In this scenario, the website stores reviews in English and must present them in the user's native language based on geographical location. This directly requires a translation workload, which uses Azure Cognitive Services - specifically, the Translator service - to automatically translate content dynamically for each user.
Other options explained:
* B. Language modeling involves predicting the next word in a sentence or understanding linguistic patterns; it's used in model training, not translation.
* C. Key phrase extraction identifies main ideas in text, not language conversion.
* D. Speech recognition converts spoken words into written text but does not perform translation or handle geographic adaptation.
Microsoft's Translator service supports real-time text translation, multi-language detection, and context preservation, making it ideal for global websites. The AI-900 study guide emphasizes translation as one of the most common NLP workloads, enabling applications to break language barriers and enhance accessibility for diverse audiences.
Therefore, based on official Microsoft Learn material, the correct answer is:
# A. translation.

Explanation:

Box1: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 2: Broad entity extraction
Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Box 3: Entity Recognition
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date
/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language- processing
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics

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

Generative AI focuses on creating new content rather than just analyzing existing data. As per Microsoft's AI-
900 curriculum and Azure OpenAI documentation, typical use cases include generating text, images, code, or other creative outputs based on input prompts.
* A. Generating draft responses for customer service agents - # Correct. GPT-based models can automatically generate draft replies to customer queries, enabling agents to refine responses and increase efficiency.
* B. Creating original artwork from textual descriptions - # Correct. DALL-E, available through Azure OpenAI, can produce unique images based on natural language prompts.
Options C and D are incorrect because they involve predictive or classification models, not generative ones:
* C. Predicting sales revenue # Regression (machine learning).
* D. Classifying email messages # Classification (machine learning).
Correct answers: A and B.

Explanation:
< A webchat bot can interact with users visiting a website # Yes
Automatically generating captions for pre-recorded videos is an example of conversational AI # No A smart device in the home that responds to questions such as "What will the weather be like today?" is an example of conversational AI # Yes
\ These answers are based on the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore conversational AI in Microsoft Azure."
1. A webchat bot can interact with users visiting a website # Yes
This statement is true. A webchat bot is a key example of conversational AI, which allows users to communicate with an intelligent system through natural language. The Azure Bot Service supports a webchat channel, enabling website visitors to ask questions or get assistance directly through a chat interface embedded on a webpage. This allows businesses to provide 24/7 automated support and interactive engagement without human intervention.
2. Automatically generating captions for pre-recorded videos is an example of conversational AI # No This is incorrect because automatically generating captions involves speech-to-text transcription, which falls under speech recognition and not conversational AI. While it uses AI to convert audio into text, it does not involve interactive communication or natural language dialogue. This task would be handled by Azure AI's Speech service, not the conversational AI framework.
3. A smart device in the home that responds to questions such as "What will the weather be like today?" is an example of conversational AI # Yes This is true. Smart assistants like those found in home devices (e.g., voice-activated systems) use conversational AI technologies to process spoken language (using natural language processing and speech recognition) and generate appropriate responses. This interaction represents a classic example of conversational AI, as it allows human-like dialogue between a user and an AI system.
# Final Answers:
* Webchat bot interacting with users # Yes
* Auto-captioning videos # No
* Smart home device answering questions # Yes