AI-102 Practice Question: Implement natural language processing solutions
You are building a chat bot that uses Azure AI Language to process customer support tickets. The bot must extract entities like order numbers (e.g., ORD-12345) and issue categories. You need to choose the best approach for entity extraction to minimize development effort and ensure high accuracy.
⚠ Common exam trap
Test-takers frequently confuse the purpose of different Azure AI Language services—specifically, they may choose CLU (Option D) thinking it is required for any NLP task, when in fact custom NER is the simpler, more appropriate choice for pure entity extraction without intent classification.
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
✓
Create a custom named entity recognition (NER) project in Azure AI Language that includes prebuilt components for order numbers and trains custom models for categories.
Azure AI Language's custom named entity recognition (NER) allows you to combine prebuilt components (like regex-based order number patterns) with custom-trained models for issue categories, minimizing development effort while achieving high accuracy. This approach leverages the built-in entity extraction capabilities of Azure AI Language without requiring intent classification or complex pipeline orchestration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Leverage the Text Analytics for Health API to extract entities from the support tickets.
Why it's wrong here
Text Analytics for Health is specialized for medical terminology and cannot extract order numbers.
- ✓
Create a custom named entity recognition (NER) project in Azure AI Language that includes prebuilt components for order numbers and trains custom models for categories.
Why this is correct
Custom NER with prebuilt components combines ease of use with flexibility for custom categories.
- ✗
Use the Prebuilt Entity Extraction skill in Azure AI Search to extract order numbers and categories from the text.
Why it's wrong here
Prebuilt Entity Extraction skill is for AI Search indexing, not for real-time entity extraction in the Language service.
- ✗
Use the Conversational Language Understanding (CLU) project type to train a model for both intent and entity extraction.
Why it's wrong here
CLU is designed for conversational flows, not just entity extraction, and requires more training data.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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