The correct choice is Custom Question Answering, because it is specifically designed to ingest official PDF documents and build a curated knowledge base of question-answer pairs that a chatbot can query directly. This Azure AI Language feature uses a natural language processing model to extract and match user questions against the ingested content, making it ideal for domain-specific, document-based Q&A scenarios like admission procedures. On the AI-900 exam, this scenario tests your understanding of how Custom Question Answering differs from other Azure AI Language features such as Conversational Language Understanding or Prebuilt QnA Maker—the key trap is confusing it with general-purpose text analytics or translation services. A common memory tip is to think of the acronym PDF: “P” for policies, “D” for documents, and “F” for FAQs, all of which Custom Question Answering directly ingests to power the chatbot.
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
This AI-900 practice question tests your understanding of describe features of natural language processing workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A university wants to build a chatbot that can answer questions about its admission procedures. The chatbot should retrieve answers directly from a set of official PDF documents containing policies and FAQs. Which Azure AI Language feature should they use to implement this?
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 allows the university to ingest official PDF documents and create a knowledge base of question-answer pairs. The chatbot can then retrieve answers directly from this curated content, making it ideal for domain-specific, document-based Q&A scenarios like admission procedures.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Sentiment analysis
Why it's wrong here
Sentiment analysis determines the emotional tone of text, not question answering from documents.
✗
Key phrase extraction
Why it's wrong here
Key phrase extraction identifies important phrases, but does not provide question-answering capabilities.
✓
Custom question answering
Why this is correct
Correct. Custom question answering builds a knowledge base from sources like PDFs and FAQs to answer user queries.
Related concept
Read the scenario before looking for a memorised answer.
✗
Language detection
Why it's wrong here
Language detection identifies the language of text, not question answering.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse key phrase extraction (B) with question answering, thinking that extracting key phrases is sufficient to answer questions, but key phrase extraction only lists terms without providing any answer retrieval or ranking logic.
Trap categories for this question
Keyword trap
Key phrase extraction identifies important phrases, but does not provide question-answering capabilities.
Detailed technical explanation
How to think about this question
Custom question answering uses a two-step process: first, it extracts question-answer pairs from documents using deep learning models; second, it ranks the most relevant answer for a user query using a transformer-based ranker. A subtle behavior is that the service can handle follow-up prompts and multi-turn conversations by maintaining context, which is critical for complex admission queries. In a real-world scenario, the university could upload PDFs of admission policies and FAQs, and the chatbot would return exact passages or pre-defined answers without needing to generate new text.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of Natural Language Processing workloads on Azure — This question tests Describe features of Natural Language Processing workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Custom question answering — Custom question answering (C) is the correct choice because it allows the university to ingest official PDF documents and create a knowledge base of question-answer pairs. The chatbot can then retrieve answers directly from this curated content, making it ideal for domain-specific, document-based Q&A scenarios like admission procedures.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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. A travel agency wants to build a chatbot that can automatically answer customer questions about flight status by extracting answers from a PDF document containing FAQs. Which Azure AI Language feature should they use to directly query this content?
easy
A.Conversational Language Understanding (CLU)
✓ B.Question Answering
C.Text Analytics for health
D.Translator
Why B: Option B, Question Answering, is correct because it is specifically designed to extract answers directly from a provided document (such as a PDF FAQ) by using a pre-built or custom knowledge base. The travel agency can upload the PDF, and the service will return precise answers to user queries without requiring intent classification or entity extraction, which is exactly what is needed for querying flight status from a static FAQ document.
Last reviewed: Jun 11, 2026
Question Discussion
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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.
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