AI-102 Practice Question: Implement natural language processing solutions
You have a custom Named Entity Recognition (NER) model trained using Azure AI Language. The model is performing poorly on new data. You need to improve its accuracy. Which action should you take first?
⚠ Common exam trap
The trap here is that candidates often jump to hyperparameter tuning (epochs) or model simplification (reducing entity types) as a quick fix, when the core issue is almost always insufficient or low-quality labeled data for specific entities, which is the first diagnostic step in any custom NER workflow.
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
✓
Review the test set results and add more labeled examples for entities with low precision/recall.
The first step to improve a custom NER model's accuracy is to analyze the test set results to identify which entity types have low precision or recall, then add more labeled examples for those specific entities. This targeted data augmentation addresses the root cause of poor performance—insufficient or imbalanced training data—rather than blindly adjusting hyperparameters or reducing complexity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the training epochs.
Why it's wrong here
May overfit without better data; not the first step.
- ✗
Retrain the model using the same training data.
Why it's wrong here
Will not improve performance as the data hasn't changed.
- ✓
Review the test set results and add more labeled examples for entities with low precision/recall.
Why this is correct
Adding targeted training data helps the model learn patterns it missed.
- ✗
Reduce the number of entity types in the model.
Why it's wrong here
May not address the root cause and could lose needed entity types.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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