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 surfaces the unlabelled documents the model is least certain about, so annotators label only the most informative examples. With 10,000 documents across 50 categories, this sharply reduces labelling effort compared with exhaustive manual annotation.
- ✗
Manually label all documents in the Language Studio
Why it's wrong here
Manual labelling of all 10,000 documents in Language Studio is slow and costly; the recommended approach labels a representative sample and lets training propagate labels to similar documents. Manual labelling suits very small datasets where every document genuinely differs.
- ✗
Use Azure Machine Learning designer to auto-label
Why it's wrong here
Azure Machine Learning designer builds and trains general ML pipelines; it does not provide the Language Studio labelling workflow for custom text classification. The intended tool for tagging documents against your 50 categories is Language Studio's labelling interface.
Go deeper
Related to this question
About these practice questions
This AI-102 question is part of Courseiva's 761-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.