AI-102 Plan and manage an Azure AI solution Practice Question
You are developing a chatbot that uses Azure AI Language to understand user intents. The chatbot must handle multiple languages and direct users to the appropriate support team based on the detected intent. Which Azure AI Language feature should you use?
⚠ Common exam trap
Many exam-takers confuse 'Text Analytics' (pre-built NLP) with 'Conversational language understanding' (custom intent/entity extraction), mistakenly thinking that Text Analytics can be trained to classify intents, when in fact it only provides pre-built capabilities like sentiment and key phrases.
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
✓
Conversational language understanding (CLU)
Conversational language understanding (CLU) is the correct feature because it is specifically designed to extract intents and entities from user utterances in a multi-language conversational context. CLU enables the chatbot to detect the user's intent (e.g., 'billing', 'technical support') and route them to the appropriate support team, while also supporting multiple languages through its language-agnostic model training and per-language project configurations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Text Analytics
Why it's wrong here
Text Analytics provides sentiment, key-phrase, entity and language detection, but it does not train or return conversational intents, so it cannot route users to a support team. It is correct when you need to extract entities or sentiment from documents rather than classify utterance intent.
- ✓
Conversational language understanding (CLU)
Why this is correct
Conversational language understanding (CLU) extracts intents and entities from utterances, and its multilingual training lets one project recognise the same intent across several languages. That satisfies the stem's requirement to detect intent and route users to the correct support team, which plain language detection or translation alone cannot do.
- ✗
QnA Maker
Why it's wrong here
QnA Maker matches question-and-answer pairs against a knowledge base; it performs neither intent classification nor language detection, so routing by detected intent across languages is impossible. It is the right choice when building a FAQ-style bot that returns curated answers to known questions.
- ✗
Translator
Why it's wrong here
Translator converts text between languages; it does not classify utterances into intents, so no routing decision can be derived from it. It is the correct component when the requirement is translating messages or documents, not understanding what the user wants to do.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 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-102 exam.