- A
Checking whether a document's references (citations) are correctly formatted
Why wrong: Citation formatting is academic writing — coreference resolution links pronouns and aliases to their referents in text.
- B
Identifying which words or phrases in a text refer to the same real-world entity
Coreference resolution links pronouns and noun phrases to their referents — 'He' → 'Satya Nadella' — essential for deep text understanding.
- C
Resolving conflicts when multiple languages are mixed in the same document
Why wrong: Code-switching is multilingual processing — coreference resolution links co-referring expressions within a document.
- D
Matching database foreign keys to their referenced primary keys
Why wrong: Database referential integrity is a data engineering concept — coreference resolution is an NLP technique for entity tracking in text.
Quick Answer
The correct answer is that coreference resolution identifies which words or phrases in a text refer to the same real-world entity. This NLP task resolves linguistic ambiguity by linking pronouns, nouns, or noun phrases that share a common referent, such as connecting “she” to “Alice” in a sentence. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how Azure AI Language services handle entity linking and document understanding, often appearing in scenarios about chatbots or summarization. A common trap is confusing coreference resolution with named entity recognition (NER), but remember: NER tags entities, while coreference resolution ties them together across mentions. For a quick memory tip, think of it as the “who’s who” of text—matching every “he,” “she,” or “it” back to its original person or thing.
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 'coreference resolution' in natural language processing?
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
Identifying which words or phrases in a text refer to the same real-world entity
Coreference resolution is the NLP task of identifying when two or more expressions in a text refer to the same real-world entity. For example, in 'Alice said she would come,' the pronoun 'she' corefers to 'Alice.' This is fundamental for tasks like document summarization and question answering, where maintaining entity consistency is critical.
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.
- ✗
Checking whether a document's references (citations) are correctly formatted
Why it's wrong here
Citation formatting is academic writing — coreference resolution links pronouns and aliases to their referents in text.
- ✓
Identifying which words or phrases in a text refer to the same real-world entity
Why this is correct
Coreference resolution links pronouns and noun phrases to their referents — 'He' → 'Satya Nadella' — essential for deep text understanding.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Resolving conflicts when multiple languages are mixed in the same document
Why it's wrong here
Code-switching is multilingual processing — coreference resolution links co-referring expressions within a document.
- ✗
Matching database foreign keys to their referenced primary keys
Why it's wrong here
Database referential integrity is a data engineering concept — coreference resolution is an NLP technique for entity tracking in text.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'coreference resolution' with 'entity extraction' (named entity recognition), but coreference resolution specifically links different mentions of the same entity, not just identifying the entity type.
Detailed technical explanation
How to think about this question
Under the hood, coreference resolution often uses neural models (e.g., BERT-based span-ranking) that compute pairwise mention scores and apply clustering algorithms like agglomerative clustering. A subtle behavior is that it must handle both anaphora (e.g., pronoun 'it' referring back to 'the document') and cataphora (e.g., 'When he arrived, John sat down'), requiring forward and backward context. In real-world Azure AI, this capability is used in the 'Analyze Text' API's entity linking and conversation summarization features to maintain coherent entity tracking across sentences.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: Identifying which words or phrases in a text refer to the same real-world entity — Coreference resolution is the NLP task of identifying when two or more expressions in a text refer to the same real-world entity. For example, in 'Alice said she would come,' the pronoun 'she' corefers to 'Alice.' This is fundamental for tasks like document summarization and question answering, where maintaining entity consistency is critical.
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
This AI-900 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-900 exam.
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