AI-102 Custom NER Practice Question
Your company uses Azure AI Language to process legal documents. You have built a custom NER model to extract parties, dates, and obligations. The model performs well on English documents but now you need to support French and German documents. You have no labeled data in those languages. You want to use the existing English model as a starting point. The solution must be cost-effective and avoid manual labeling as much as possible. You also need to ensure that the model can be retrained quickly when new document types are added. Which approach should you take?
⚠ Common exam trap
A common trap is to assume that labeled data is needed for each target language, but Azure AI Language's custom NER supports multilingual training, allowing a model trained on English data to generalize to other languages.
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
✓
Enable the multilingual option in the custom NER project and retrain the model using the existing English labeled data.
Enabling the multilingual option in custom NER allows training on the existing English labeled data, and the model can generalize to French and German without requiring labeled data in those languages. This is cost-effective and avoids manual labeling. Option A is wrong because using the prebuilt NER would not extract custom entities like parties, dates, and obligations in the specific format needed. Option C is wrong because translating documents introduces potential errors and latency, and may not capture legal nuances. Option D is wrong because building separate models requires translating the labeled data, which is additional effort and may lose context.
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 the prebuilt NER model for French and German to extract entities like dates and organizations, and then map them to your custom entity types.
Why it's wrong here
Prebuilt NER does not extract custom entities like 'parties' and 'obligations' as defined in your model; the mapping would be inaccurate and incomplete.
- ✓
Enable the multilingual option in the custom NER project and retrain the model using the existing English labeled data.
Why this is correct
The multilingual option allows the model to learn from English data and predict entities in French and German without additional labeled data, meeting cost-effectiveness and avoiding manual labeling.
- ✗
Use the English custom NER model as-is and translate the French and German documents to English before processing.
Why it's wrong here
Translation introduces latency and potential errors, especially for legal terminology; also, the model may not capture language-specific nuances.
- ✗
Build separate custom NER models for French and German by translating the English labeled data using Azure AI Translator and training new models.
Why it's wrong here
This requires translation effort and training multiple models; the multilingual option is simpler and often performs well.
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