AI-102 Practice Question: Implement natural language processing solutions
Which TWO actions should you take to improve the performance of a custom named entity recognition (NER) model in Azure AI Language?
⚠ Common exam trap
A common mix-up: candidates confuse 'more training time' with 'better learning,' but Azure AI Language's training process automatically stops when validation loss plateaus, making extended training ineffective without additional data.
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
✓
Use a balanced dataset with similar numbers of examples for each entity.
A balanced dataset ensures the model learns to recognize all entity types equally, preventing bias toward overrepresented entities. In Azure AI Language custom NER, the model's performance depends on the quality and distribution of labeled data; an imbalanced dataset can lead to poor recall for minority 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.
- ✓
Use a balanced dataset with similar numbers of examples for each entity.
Why this is correct
Balanced data prevents bias towards certain entities.
- ✗
Increase the training time of the model.
Why it's wrong here
Training time is not user-configurable.
- ✗
Reduce the number of entity types to simplify the model.
Why it's wrong here
May lose needed entity types.
- ✓
Label more examples with entity annotations.
Why this is correct
More training data improves model performance.
- ✗
Use only prebuilt entity types to avoid training from scratch.
Why it's wrong here
Prebuilt entities are not part of custom NER training.
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