AI-102 Practice Question: Implement natural language processing solutions
Which THREE components are required to build a custom question answering solution using Azure AI Language?
⚠ Common exam trap
AI-102 often tests the misconception that LUIS or Bot Service are required for question answering, confusing the separate Azure AI services and their roles.
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
✓
A project in Azure AI Language
Option B is correct because a custom question answering solution in Azure AI Language is created as a project (formerly a knowledge base) within the Azure AI Language resource, which holds the Q&A data and configuration. Option E is correct because the project must contain a knowledge base populated with question-and-answer pairs (or imported sources such as FAQs and documents) that the service indexes and searches. Option C is correct because after the knowledge base is built and deployed, a query endpoint is required so client applications can send questions and receive answers via the REST API or SDK. Option A is not needed because LUIS is a separate language understanding service for intent and entity extraction, not for custom question answering. Option D is not required because Azure AI Bot Service is only an optional client that can consume the endpoint; the question answering solution itself does not depend on it.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A Language Understanding (LUIS) app
Why it's wrong here
LUIS is a separate conversational language understanding service; custom question answering in Azure AI Language requires a Language resource, a project and knowledge sources. LUIS is tempting because it also builds language models, but it handles intent classification, not question answering pairs.
- ✓
A project in Azure AI Language
Why this is correct
Custom question answering stores question-answer pairs, sources and trained model versions inside an Azure AI Language project. The project defines the task type and is the prerequisite container for importing sources and training the knowledge base.
- ✓
An endpoint to query the knowledge base
Why this is correct
After training and deploying, the knowledge base is consumed through a prediction endpoint that accepts user questions and returns ranked answers. Without a deployed endpoint, client applications cannot query the knowledge base, so it is a required component.
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An Azure AI Bot Service resource
Why it's wrong here
Azure AI Bot Service hosts the conversational client, not the question answering knowledge base; the required components are the Azure AI Language resource, a project and its sources. Bot Service is correct when deploying a bot that consumes the answer endpoint, which is optional here.
- ✓
A knowledge base with question and answer pairs
Why this is correct
A knowledge base holding question-and-answer pairs is the core artefact the custom question answering project trains and queries against; without it, no answer can be matched or returned. It satisfies the requirement for a project built on Azure AI Language's question answering capability.
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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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