AI-103 Text Analysis Practice Question
You are building a custom text classification solution using Azure AI Language in Language Studio. You have a dataset where each document can belong to multiple categories simultaneously (e.g., a news article can be tagged as both 'Technology' and 'Finance'). What classification type should you configure?
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
✓
Multi-label classification
Multi-label classification allows a document to be associated with zero, one, or multiple classes simultaneously, whereas single-label classification restricts each document to exactly one class.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Custom named entity recognition
Why it's wrong here
Custom NER extracts specialized entities from text rather than classifying whole documents.
- ✓
Multi-label classification
Why this is correct
Multi-label classification is designed for scenarios where a single document can be assigned multiple overlapping categories.
- ✗
Single-label classification
Why it's wrong here
Single-label classification requires each document to be mapped to strictly one category.
- ✗
Extractive summarization
Why it's wrong here
Extractive summarization extracts key sentences from documents, not classify them into categories.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-103 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-103 exam.