Describe features of Natural Language Processing workloads on Azure →mediumMultiple ChoiceObjective-mapped
Custom Question Answering for FAQ Bots
A company wants to build a self-service FAQ bot that answers customer questions based on a collection of policy documents (PDFs and Word files). They want the bot to extract answers directly from the documents without manually creating question-answer pairs. Which Azure AI Language feature should they use?
Quick Answer
The answer is Custom Question Answering, a feature within Azure AI Language that is the correct choice for building a self-service FAQ bot from policy documents. This feature uses a deep learning-based extractive reader to locate exact answer spans directly within PDFs and Word files, eliminating the need to manually create question-answer pairs. On the AI-900 exam, this scenario tests your understanding of how Custom Question Answering differs from pre-built FAQ solutions or traditional QnA Maker, which required manual pair creation. A common trap is confusing it with Conversational Language Understanding, but remember: CLU is for intent classification and entity extraction from user utterances, not for extracting answers from documents. For the exam, focus on the key phrase “extract answers directly from source documents” as your trigger for Custom Question Answering. Memory tip: think “Custom QA = Custom extraction from docs,” where the bot reads the policy files like a student skimming a textbook for the exact answer.
⚠ Common exam trap
It's easy for candidates to confuse Custom Question Answering with Conversational Language Understanding (CLU) because both involve 'language understanding,' but CLU requires manual intent/entity creation and does not extract answers from documents, while Custom Question Answering is purpose-built for extractive QA from uploaded files.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Custom Question Answering
Custom Question Answering (C) is the correct choice because it is specifically designed to extract answers directly from source documents (PDFs, Word files) without requiring manual creation of question-answer pairs. It uses a deep learning-based extractive reader to locate answer spans within the text, making it ideal for building a self-service FAQ bot from policy documents.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Key Phrase Extraction
Why it's wrong here
Key Phrase Extraction identifies important concepts in text but does not support answering questions based on document content.
- ✗
Conversational Language Understanding (CLU)
Why it's wrong here
CLU is used for intent recognition and entity extraction in conversational contexts, not for extracting answers from documents.
- ✓
Custom Question Answering
Why this is correct
Custom Question Answering enables you to create a knowledge base from documents and automatically answer user questions using the content.
- ✗
Azure OpenAI on your data
Why it's wrong here
While Azure OpenAI can be used with your data, it is a generative AI service, not part of the core Azure AI Language NLP features. The question specifically asks for an Azure AI Language feature.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Question answering
Question answering is an Azure AI service that lets you build a natural language query system that extracts answers from a custom knowledge base of documents or FAQs.
Key term
Azure AI Language
Azure AI Language is a cloud-based service from Microsoft that uses natural language processing to understand, analyze, and generate human language for applications.
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Same concept, more angles
1 more way this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. What is 'question answering' in Azure AI Language and what are its two main types?
medium- A.Multiple-choice question generation and open-ended answer scoring
- ✓ B.Custom QA (trained on your documents) and prebuilt QA (document provided at query time)
- C.Structured QA for databases and unstructured QA for text documents
- D.Real-time QA for chatbots and batch QA for scheduled document processing
Why B: Azure AI Language's 'question answering' feature provides two distinct capabilities: Custom QA, where you train a model on your own documents (e.g., PDFs, FAQs) to answer questions from that knowledge base, and Prebuilt QA, which uses a document provided at query time to extract answers without prior training. This distinction is fundamental to how the service is deployed—either as a persistent, trained knowledge base or as an on-the-fly extraction from a user-supplied document.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.