Implement knowledge mining and information extraction solutions →mediumMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
You are using Azure AI Language to extract information from medical research papers. You need to identify terms like 'dosage', 'side effects', and 'contraindications' specific to the medical domain. Which capability should you use?
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
✓
Custom Named Entity Recognition (NER)
Custom Named Entity Recognition allows you to train a model to recognize custom entities like medical terms. Option A is wrong because prebuilt NER only recognizes general entities like person, location, etc. Option C is wrong because PII detection is for personal information. Option D is wrong because entity linking links to external knowledge bases.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Prebuilt Named Entity Recognition (NER)
Why it's wrong here
Prebuilt NER does not recognize domain-specific medical terms.
- ✓
Custom Named Entity Recognition (NER)
Why this is correct
Custom NER allows you to train a model on your specific domain vocabulary.
- ✗
PII detection
Why it's wrong here
PII detection identifies sensitive personal data, not medical terms.
- ✗
Entity linking
Why it's wrong here
Entity linking disambiguates to knowledge bases, not custom entities.
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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.