AI-102 Practice Question: Implement knowledge mining and information extraction solutions
You are designing a knowledge mining solution for a medical research organization. The solution must extract relationships between drugs, diseases, and genes from scientific articles. The data will be stored in a knowledge graph for querying. Which Azure AI service should you use for the extraction?
⚠ Common exam trap
Candidates often confuse general-purpose text extraction (Azure AI Document Intelligence) or search (Azure AI Search) with domain-specific biomedical entity and relation extraction, which requires a specialized healthcare NLP model like Azure AI Language's healthcare feature.
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
✓
Azure AI Language healthcare entity recognition and relation extraction
Azure AI Language's healthcare entity recognition and relation extraction is specifically designed to extract medical entities (drugs, diseases, genes) and their relationships from unstructured text, making it ideal for building a knowledge graph. This pre-built model uses deep learning trained on biomedical literature, directly supporting the required extraction without custom training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure AI Search with semantic ranking
Why it's wrong here
Azure AI Search with semantic ranking indexes and ranks documents for retrieval; it does not extract entity relationships into a graph. It is tempting because it is central to knowledge mining pipelines, and would be correct for querying and ranking content once relationships had already been extracted by another service.
- ✗
Azure AI Translator with dictionary lookup
Why it's wrong here
Azure AI Translator with dictionary lookup translates text and returns alternate translations for terms; it performs no relationship extraction. It is tempting because dictionary lookup appears to map terminology, and would be correct for multilingual translation or custom term glossaries rather than building a knowledge graph.
- ✗
Azure AI Document Intelligence custom extraction model
Why it's wrong here
Document Intelligence custom models extract structured fields from forms and documents, not relationships between drugs, diseases and genes across article text. It is tempting because it handles document extraction in knowledge mining, and would be correct for pulling defined fields such as dates or dosages from structured layouts.
- ✓
Azure AI Language healthcare entity recognition and relation extraction
Why this is correct
Healthcare entity recognition extracts drugs, diseases and genes as typed entities, and relation extraction links them, producing exactly the drug-disease-gene relationships the knowledge graph requires. General entity extraction cannot capture these biomedical relation types, so it fails the graph-querying requirement.
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Same concept, more angles
1 more way this is tested on AI-102
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A healthcare organization is implementing a knowledge mining solution to extract information from medical records. They need to ensure that the solution can identify medical conditions, medications, and treatment procedures using a pre-built model. The solution must be deployed in Microsoft Foundry. Which THREE components should be included? (Choose three.)
medium- ✓ A.Text Analytics for Health skill in an Azure AI Search skillset.
- ✓ B.Azure AI Search index.
- ✓ C.Text Analytics for Health model in Microsoft Foundry.
- D.Azure AI Document Intelligence (formerly Form Recognizer) custom model.
- E.Language Understanding (LUIS) model.
Why A: Option A is correct because the Text Analytics for Health skill is the built-in Azure AI Search cognitive skill that invokes the healthcare NLP model to extract entities such as medical conditions, medications, and treatment procedures from unstructured medical record text during skillset enrichment. Option B is correct because an Azure AI Search index is required to store the enriched documents and their extracted entities so the knowledge mining solution can be queried and surfaced. Option C is correct because the Text Analytics for Health model in Microsoft Foundry is the pre-built model that performs the entity extraction for medical conditions, medications, and procedures, and it is the Foundry-hosted capability the scenario requires. Option D is not correct because Azure AI Document Intelligence custom models are trained for form and document layout extraction, not for identifying clinical entities like conditions and medications. Option E is not correct because LUIS is a conversational language understanding service for intents and utterances, not a pre-built medical entity extraction model.
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.