AI-102 Practice Question: Implement natural language processing solutions
Which THREE components are required to build a custom named entity recognition (NER) model in Azure Cognitive Service for Language?
⚠ Common exam trap
A common mix-up: candidates confuse the separate Azure AI services (QnA Maker, LUIS, Language service) and assume they are interchangeable for custom NER, when in fact each has a distinct role and pipeline.
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 set of labeled documents for training.
A custom NER model in Azure Cognitive Service for Language requires a set of labeled documents for training. These labeled documents define the entities and their spans within text, which the model uses to learn patterns for extraction. Without labeled data, the model cannot be trained to recognize custom entities.
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 QnA Maker knowledge base for entity definitions.
Why it's wrong here
QnA Maker is for question answering, not NER.
- ✗
A LUIS application to handle entity extraction.
Why it's wrong here
LUIS is a separate service for conversational NLU.
- ✓
A set of labeled documents for training.
Why this is correct
Labeled data is essential for training.
- ✓
A language service project with a custom NER schema.
Why this is correct
The project defines entity types and tags.
- ✓
A deployed endpoint for the custom NER model.
Why this is correct
The model must be deployed for inferencing.
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