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 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?
Why wrong: CLU is designed for intent detection and entity extraction within a conversational flow, not for directly importing Q&A pairs from a document.
B
Custom Text Classification
Why wrong: Custom Text Classification assigns predefined labels to text, but it does not create a Q&A knowledge base for a chatbot.
C
Custom Question Answering
Custom Question Answering allows you to import a FAQ document or other structured content to build a knowledge base that a chatbot can query to answer user questions.
D
Language Detection
Why wrong: Language Detection identifies the language of a text, but it does not provide question-answering capabilities.
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 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.
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.
✗
Conversational Language Understanding (CLU)
Why it's wrong here
CLU is designed for intent detection and entity extraction within a conversational flow, not for directly importing Q&A pairs from a document.
✗
Custom Text Classification
Why it's wrong here
Custom Text Classification assigns predefined labels to text, but it does not create a Q&A knowledge base for a chatbot.
✓
Custom Question Answering
Why this is correct
Custom Question Answering allows you to import a FAQ document or other structured content to build a knowledge base that a chatbot can query to answer user questions.
Related concept
Read the scenario before looking for a memorised answer.
✗
Language Detection
Why it's wrong here
Language Detection identifies the language of a text, but it does not provide question-answering capabilities.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Conversational Language Understanding (CLU) with Custom Question Answering, but CLU requires custom code for intent handling and does not natively support a static Q&A knowledge base, whereas Custom Question Answering is purpose-built for this exact use case.
Detailed technical explanation
How to think about this question
Custom Question Answering in Azure AI Language uses a ranker model that applies a deep learning-based transformer to match user queries against the knowledge base, returning the highest-confidence answer. It supports active learning to improve answer accuracy over time by suggesting alternative phrasings from user interactions. In a real-world scenario, this feature can be integrated with Azure Bot Service to deploy a fully functional chatbot with minimal development effort.
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 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.
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.
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Question Discussion
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