AI-103 Text Analysis Practice Question
You are analyzing legal contracts using Azure AI Language custom named entity recognition. You notice that overlapping entities (e.g., 'Acme Corporation' inside a larger entity 'Acme Corporation Headquarters') are causing extraction errors in your custom model. How should you resolve this in Language Studio?
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
✓
Configure the project schema to support overlapping entities during project creation.
When configuring custom NER projects, you can enable or configure support for overlapping entities during project creation if your schema requires nested or overlapping extractions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure the project schema to support overlapping entities during project creation.
Why this is correct
Custom NER projects require enabling overlapping entities settings at project creation if nested or intersecting tags occur.
- ✗
Increase the confidence score threshold to 0.99.
Why it's wrong here
Threshold adjustments filter out lower-confidence scores but do not alter schema overlap handling.
- ✗
Switch the project type from custom NER to custom text classification.
Why it's wrong here
Classification categorizes documents; it does not extract entities.
- ✗
Convert all contract texts to lowercase before tagging.
Why it's wrong here
Case sensitivity settings can be adjusted, but lowercasing does not resolve overlapping structural tags.
Visual reference
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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.