The answer is Custom Question Answering, part of Azure AI Language. This service is the correct choice because it allows you to build an FAQ bot from a knowledge base without custom training, using a built-in semantic ranking model to match user intent to pre-defined answers even when questions are phrased differently. On the AI-900 exam, this question tests your understanding of pre-built AI services versus custom ML models; a common trap is confusing Custom Question Answering with Azure Bot Service or LUIS, but remember that LUIS requires custom training, while Custom Question Answering directly ingests your FAQ documents and handles varied phrasing out of the box. The exam emphasizes that this feature is designed for exactly this scenario—matching varied user questions to fixed answers from a knowledge base without any additional model training. A helpful memory tip: think of it as “FAQ in, answers out, no training required.”
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 company wants to build an FAQ bot that can answer questions based on its internal knowledge base. The questions from users are often phrased in different ways. They want to match the user's intent to pre-defined answers without training a custom model. Which Azure AI Language feature should they use?
Custom Question Answering is designed to create a knowledge base and match user questions to pre-defined answers, even with varied phrasing.
B
Language Understanding (LUIS)
Why wrong: LUIS requires training a custom model to extract intents and entities. The scenario specifies 'without training a custom model', so LUIS is not appropriate.
C
Translator
Why wrong: Azure Translator is for translating text between languages, not for answering FAQs from a knowledge base.
D
Sentiment Analysis
Why wrong: Sentiment analysis determines the emotional tone of text, not for matching questions to answers.
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 (formerly QnA Maker) is the correct choice because it allows you to ingest a knowledge base (e.g., FAQs, manuals) and match user questions to pre-defined answers using a built-in ranking model, without training a custom ML model. It handles varied phrasing through semantic understanding and returns the best answer from the curated content, directly addressing the requirement to match intent to pre-defined answers without custom training.
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.
✓
Custom Question Answering
Why this is correct
Custom Question Answering is designed to create a knowledge base and match user questions to pre-defined answers, even with varied phrasing.
Related concept
Read the scenario before looking for a memorised answer.
✗
Language Understanding (LUIS)
Why it's wrong here
LUIS requires training a custom model to extract intents and entities. The scenario specifies 'without training a custom model', so LUIS is not appropriate.
✗
Translator
Why it's wrong here
Azure Translator is for translating text between languages, not for answering FAQs from a knowledge base.
✗
Sentiment Analysis
Why it's wrong here
Sentiment analysis determines the emotional tone of text, not for matching questions to answers.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Custom Question Answering with LUIS, thinking both require custom training, but Custom Question Answering uses a pre-built ranking model that works out-of-the-box with a knowledge base, while LUIS requires explicit intent and entity labeling.
Trap categories for this question
Scenario analysis trap
LUIS requires training a custom model to extract intents and entities. The scenario specifies 'without training a custom model', so LUIS is not appropriate.
Detailed technical explanation
How to think about this question
Under the hood, Custom Question Answering uses a transformer-based ranker that compares the user's query against all question-answer pairs in the knowledge base, returning the highest-scoring match along with a confidence score. It supports active learning, where it can suggest alternative phrasings from user queries to improve future matches, but this is optional and does not require custom model training. In a real-world scenario, a company could upload a support FAQ document, and the bot would handle rephrased questions like 'How do I reset my password?' and 'I forgot my login credentials' by mapping both to the same pre-defined answer about password reset steps.
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 (formerly QnA Maker) is the correct choice because it allows you to ingest a knowledge base (e.g., FAQs, manuals) and match user questions to pre-defined answers using a built-in ranking model, without training a custom ML model. It handles varied phrasing through semantic understanding and returns the best answer from the curated content, directly addressing the requirement to match intent to pre-defined answers without custom training.
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
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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 company wants to build a chatbot that can answer customer questions about their product return policy, shipping times, and warranty information. They have a structured document with these questions and answers. Which Azure AI Language feature should they use to create this chatbot without writing custom code?
medium
A.Conversational Language Understanding (CLU)
B.Custom Text Classification
✓ C.Custom Question Answering
D.Language Detection
Why C: Custom Question Answering (C) is the correct choice because it is specifically designed to create a chatbot that answers questions based on a structured document (e.g., FAQ, product manual) without writing custom code. It uses a predefined knowledge base of question-answer pairs and provides a built-in orchestration for bot integration, making it ideal for this scenario.
Variation 2. A customer support team wants to create a chatbot that can answer common questions about employee benefits. They have a PDF document containing a list of frequently asked questions with their answers. Which Azure AI Language feature should they use to build a solution that extracts answers directly from this content?
medium
A.Sentiment Analysis
B.Key Phrase Extraction
✓ C.Custom Question Answering
D.Text Analytics for Health
Why C: Custom Question Answering (C) is the correct choice because it is specifically designed to ingest documents like PDFs and extract question-answer pairs from them, enabling a chatbot to respond directly with answers from the content. This feature uses a pre-built or custom knowledge base to match user queries to the most relevant answer, making it ideal for the described scenario.
Variation 3. A company's HR department wants to create a self-service bot that can answer employee questions about company policies. They have a collection of policy documents in PDF format. Which Azure AI Language feature should they use to ingest these documents and enable the bot to provide answers based on them?
hard
A.Sentiment Analysis
B.Key Phrase Extraction
✓ C.Custom Question Answering
D.Language Detection
Why C: Custom Question Answering (CQA) is the correct choice because it is specifically designed to ingest documents (including PDFs) and build a knowledge base of question-answer pairs. The bot can then query this knowledge base to provide answers based on the policy documents, using the underlying Azure Cognitive Search and language models to match user questions to the most relevant content.
Variation 4. Which Azure service enables building a question-answering chatbot from existing FAQ documents with minimal code?
medium
A.Azure Machine Learning with custom NLP models
✓ B.Azure AI Language question answering + Azure Bot Service
C.Azure AI Speech with custom commands
D.Azure Cognitive Search alone
Why B: Azure AI Language question answering (formerly QnA Maker) is specifically designed to extract question-answer pairs from semi-structured content like FAQ documents, PDFs, and URLs, and expose them via a REST API. Azure Bot Service provides the conversational interface and channel integration (e.g., Teams, Web Chat) with minimal code. Together, they enable a fully functional Q&A chatbot without custom NLP model training.
Variation 5. What is the question answering feature in Azure AI Language used for?
medium
A.Training custom language models for complex conversations
✓ B.Building knowledge bases that automatically answer questions from FAQ content
C.Generating new questions from a given text
D.Testing the quality of chatbot responses
Why B: The question answering feature in Azure AI Language is designed to extract answers from structured content like FAQs, manuals, or support documents. It builds a knowledge base that can automatically respond to user queries in natural language, making it ideal for customer support or self-service portals.
Last reviewed: Jun 11, 2026
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