AI-102 Practice Question: Implement natural language processing solutions
You are deploying a custom named entity recognition (NER) model using Azure AI Language. The model must extract product codes that follow a specific pattern (e.g., 'PRD-12345'). You have 5,000 labeled examples. After training, the model extractor works well on development data but fails to extract product codes from new data. What is the most likely issue?
⚠ Common exam trap
Candidates often assume insufficient training data (Option A) is the cause of poor generalization, but the question explicitly states 5,000 labeled examples—a typical sufficient amount—and the key clue is the performance gap between development and new data, which points directly to overfitting.
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 is overfitting to the training data.
The model performs well on development data but fails on new data, which is the classic symptom of overfitting. In Azure AI Language custom NER, overfitting occurs when the model memorizes the training examples—including noise or specific patterns—rather than generalizing to the underlying product code pattern. With 5,000 labeled examples, the dataset size is likely sufficient, but the model may have learned spurious correlations that do not hold in unseen data.
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 training data size is insufficient.
Why it's wrong here
5,000 labeled examples is typically sufficient for custom NER.
- ✗
The product code pattern is too complex for the model to learn.
Why it's wrong here
The model can learn patterns, but overfitting is the issue.
- ✓
The model is overfitting to the training data.
Why this is correct
Overfitting causes good performance on training data but poor on new data.
- ✗
The labeling is inconsistent across the dataset.
Why it's wrong here
Inconsistency would cause poor performance on both dev and new data.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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