- A
Creating hyperlinks in a document that connect to related content online
Why wrong: Hyperlink creation is document editing — entity linking connects identified entities to knowledge base entries for disambiguation.
- B
Linking identified entities to knowledge base entries (e.g., Wikipedia) for disambiguation
Entity linking resolves ambiguity — 'Mars' could be the planet or a chocolate bar; linking to Wikipedia disambiguates.
- C
Connecting named entities across multiple documents to track the same person over time
Why wrong: Cross-document entity tracking is co-reference resolution across documents — entity linking maps to a knowledge base, not other documents.
- D
Linking entity recognition results to downstream API calls for data enrichment
Why wrong: API integration is application architecture — entity linking is the specific NLP task of grounding entities to knowledge base records.
Quick Answer
The correct answer is that entity linking in Azure AI Language identifies entities and then disambiguates them by associating each with a unique identifier from a knowledge base, such as Wikipedia’s Q-numbers. This goes beyond Named Entity Recognition (NER), which simply labels entities as categories like “person” or “location” without resolving ambiguity—for example, NER would label “Paris” as a location, but entity linking would determine whether it refers to the city in France or the mythological figure by linking to the correct knowledge base entry. On the AI-900 exam, this distinction tests your understanding of how Azure AI Language adds context to raw entity extraction; a common trap is assuming NER already handles disambiguation. Remember, NER tells you *what* something is, while entity linking tells you *which specific one* it is. Memory tip: think of NER as a label maker and entity linking as a librarian who finds the exact book on the shelf.
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
This AI-900 practice question tests your understanding of describe features of natural language processing workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
What is 'entity linking' in Azure AI Language and how does it differ from NER?
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
Linking identified entities to knowledge base entries (e.g., Wikipedia) for disambiguation
Entity linking in Azure AI Language disambiguates identified entities by associating them with a unique identifier from a knowledge base, such as Wikipedia's Q-numbers. This differs from NER, which only labels entities (e.g., 'person', 'location') without resolving ambiguity—for example, 'Paris' could refer to a city or a person, and entity linking determines the correct one via the knowledge base.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Creating hyperlinks in a document that connect to related content online
Why it's wrong here
Hyperlink creation is document editing — entity linking connects identified entities to knowledge base entries for disambiguation.
- ✓
Linking identified entities to knowledge base entries (e.g., Wikipedia) for disambiguation
Why this is correct
Entity linking resolves ambiguity — 'Mars' could be the planet or a chocolate bar; linking to Wikipedia disambiguates.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Connecting named entities across multiple documents to track the same person over time
Why it's wrong here
Cross-document entity tracking is co-reference resolution across documents — entity linking maps to a knowledge base, not other documents.
- ✗
Linking entity recognition results to downstream API calls for data enrichment
Why it's wrong here
API integration is application architecture — entity linking is the specific NLP task of grounding entities to knowledge base records.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing entity linking with NER's simple labeling—candidates often think NER already handles disambiguation, but NER only tags entity types, while entity linking resolves which specific entity is meant.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Language's entity linking uses a pre-trained model that maps entity mentions to the Bing Entity Search knowledge graph, which includes Wikipedia identifiers. For example, 'Washington' in 'Washington was the first president' is linked to Q23 (George Washington), while 'Washington' in 'I live in Washington' links to Q61 (Washington, D.C.). This disambiguation relies on context vectors and a large-scale knowledge base, enabling precise entity resolution even with ambiguous terms.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of Natural Language Processing workloads on Azure — This question tests Describe features of Natural Language Processing workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Linking identified entities to knowledge base entries (e.g., Wikipedia) for disambiguation — Entity linking in Azure AI Language disambiguates identified entities by associating them with a unique identifier from a knowledge base, such as Wikipedia's Q-numbers. This differs from NER, which only labels entities (e.g., 'person', 'location') without resolving ambiguity—for example, 'Paris' could refer to a city or a person, and entity linking determines the correct one via the knowledge base.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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