AI-102 Practice Question: Implement natural language processing solutions
You are designing a solution that must extract specific entities from customer emails, such as product names, order numbers, and dates. The solution must be able to learn from a small set of labeled examples and improve over time. Which Azure AI service should you use?
⚠ Common exam trap
Watch out — candidates often confuse pre-built entity extraction (which is out-of-the-box but inflexible) with custom entity extraction (which requires training but adapts to specific needs), leading them to choose Option B because they assume 'pre-built' means 'easier' without recognizing the requirement for custom entities.
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
✓
Custom Entity Extraction
Custom Entity Extraction (D) is correct because it allows you to train a model with a small set of labeled examples to extract domain-specific entities like product names, order numbers, and dates from customer emails. This service supports iterative learning and improvement over time, making it ideal for scenarios where pre-built models lack the required specificity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
QnA Maker
Why it's wrong here
QnA Maker builds a question-and-answer knowledge base matched to user queries; it performs no entity extraction and cannot learn custom entities from labelled examples. Custom named entity recognition is the service for that task, whereas QnA Maker suits FAQ-style conversational answers.
- ✗
Pre-built Entity Extraction
Why it's wrong here
Pre-built entity extraction returns only fixed categories such as person, location and organisation, so it cannot learn custom product names or order numbers from labelled examples. Custom named entity recognition is the service that trains on your labelled data; pre-built suits generic extraction with no training.
- ✗
Text Analytics for health
Why it's wrong here
Text Analytics for health extracts medical entities such as medications, diagnoses and dosages from clinical text, not product names or order numbers. Custom named entity recognition trains on your labelled examples to extract bespoke entities; the health model suits clinical documents only.
- ✓
Custom Entity Extraction
Why this is correct
Custom Entity Extraction learns from a small set of labelled examples, letting you define product names, order numbers and dates as custom entities and retrain as more data arrives. This satisfies the requirement to improve over time, unlike prebuilt extraction which cannot adapt to your specific entity schema.
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