Question 793 of 945
AI-102 Practice Question: Implement natural language processing solutions
A developer is creating a custom text classification model using Azure AI Language. The dataset has 10,000 documents across 50 categories. Which method is most suitable for labeling the data efficiently?
⚠ Common exam trap
A common mix-up: candidates assume 'prebuilt models' (Option A) can be adapted to custom categories via fine-tuning, but Microsoft Azure AI Language custom text classification requires a dedicated project with active learning—prebuilt models are static and cannot learn new labels.
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 active learning in the custom text classification project
Active learning in custom text classification projects automatically selects the most informative unlabeled documents for manual review, reducing labeling effort while maximizing model accuracy. With 10,000 documents across 50 categories, active learning prioritizes ambiguous or high-uncertainty samples, making it the most efficient approach for iterative labeling.
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 prebuilt model from the Azure AI Language service
Why it's wrong here
Prebuilt models cannot handle custom categories.
- ✓
Use active learning in the custom text classification project
Why this is correct
Active learning suggests labels, reducing manual effort.
- ✗
Manually label all documents in the Language Studio
Why it's wrong here
Manual labeling of 10k docs is inefficient.
- ✗
Use Azure Machine Learning designer to auto-label
Why it's wrong here
Azure ML designer is not designed for text classification labeling.
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Last reviewed: Jul 4, 2026
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