AI-102 Plan and manage an Azure AI solution Practice Question
Your Azure AI Language custom entity recognition model incorrectly extracts 'Microsoft' as an organization when it refers to the company, but fails to extract 'Microsoft' as a product when it refers to the software. How should you improve the model?
⚠ Common exam trap
Test-takers frequently think reducing data or removing entity types simplifies the problem, but Azure AI Language models require diverse, labeled examples with context to handle polysemy (same word, different meanings).
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
✓
Label 'Microsoft' as both 'Organization' and 'Product' in different training sentences with appropriate context
Custom entity recognition models in Azure AI Language learn to distinguish entity types based on context. By labeling 'Microsoft' as 'Organization' in sentences where it refers to the company and as 'Product' in sentences where it refers to the software, you provide the model with the contextual clues needed to disambiguate the same token across different uses. This supervised learning approach directly addresses the model's failure to recognize the product entity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the amount of training data to avoid confusion
Why it's wrong here
Reducing data would likely degrade model performance.
- ✗
Remove the 'Organization' entity type from the model
Why it's wrong here
Removing the type would prevent extraction of 'Microsoft' as an organization, which is correct.
- ✗
Add more training sentences without labeling the entity type
Why it's wrong here
Unlabeled data does not teach the model to distinguish entity types.
- ✓
Label 'Microsoft' as both 'Organization' and 'Product' in different training sentences with appropriate context
Why this is correct
Providing multiple entity types for the same word helps the model learn context-based disambiguation.
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