AI-102 Practice Question: Implement natural language processing solutions
A research organization uses Azure AI Language to process large volumes of scientific papers. They need to extract specific entities such as gene names, protein names, and chemical compounds. The entity types are highly specialized and not covered by prebuilt models. The organization has a labeled dataset of 10,000 documents. You need to recommend the most efficient approach to build the entity extraction solution. What should you do?
⚠ Common exam trap
Many candidates assume prebuilt NER can be adapted via mapping or post-processing, but Azure AI Language's prebuilt models are fixed and cannot recognize custom entity types without training a custom model.
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
✓
Train a Custom Named Entity Recognition (NER) model using the labeled dataset in Azure AI Language.
Custom Named Entity Recognition (NER) in Azure AI Language allows you to train a model on your own labeled dataset (10,000 documents) to extract highly specialized entity types like gene names, protein names, and chemical compounds that are not covered by prebuilt models. This approach is the most efficient as it leverages the labeled data directly, avoiding the need for complex post-processing or hybrid solutions.
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 and map the recognized entities to the required types.
Why it's wrong here
Prebuilt NER does not recognize specialized entities like gene and protein names.
- ✓
Train a Custom Named Entity Recognition (NER) model using the labeled dataset in Azure AI Language.
Why this is correct
Custom NER allows training a model tailored to the specific entities using the labeled data.
- ✗
Use Azure Logic Apps to call the Text Analytics API and post-process the results.
Why it's wrong here
Logic Apps is an integration service and does not solve the entity extraction problem.
- ✗
Train a custom NER model for genes and use prebuilt NER for chemicals.
Why it's wrong here
This approach duplicates effort; a single custom NER model can handle all entity types.
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