AI-102 Practice Question: Implement natural language processing solutions
A healthcare organization deploys an Azure AI Language Service custom entity recognition model to extract medical conditions from clinical notes. During testing, the model fails to recognize rare diseases mentioned in the training data. What is the most likely cause?
⚠ Common exam trap
Test-takers frequently confuse data imbalance (Option D) with insufficient labeled examples, but the core issue is that rare diseases lack enough training instances for the model to learn their contextual patterns, not just that common diseases dominate the dataset.
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
✓
The model requires more labeled examples of rare diseases
The custom entity recognition model in Azure AI Language Service requires sufficient labeled examples for each entity type to learn patterns effectively. Rare diseases with few training examples will have lower recall because the model lacks the statistical evidence to generalize those entities. This is a classic data sparsity issue in supervised NLP models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The model requires more labeled examples of rare diseases
Why this is correct
Custom entity models need adequate examples per entity; rare diseases often lack sufficient labeled data.
- ✗
The entity length exceeds the maximum allowed for the model
Why it's wrong here
Entity length is not typically a limiting factor.
- ✗
The model is overfitting to common diseases
Why it's wrong here
Overfitting would still recognize rare diseases if they were in training data.
- ✗
The training data is imbalanced with too many common diseases
Why it's wrong here
Imbalance alone does not explain failure to recognize rare diseases.
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