- A
Correcting spelling mistakes caused by homophones (words that sound the same)
Why wrong: Homophone correction is spell-checking — WSD determines which sense of a polysemous word is intended in context.
- B
Determining which meaning of an ambiguous word is intended based on surrounding context
WSD resolves lexical ambiguity — understanding 'bank' means financial institution vs. riverbank based on sentence context.
- C
Translating words from one language to their exact equivalent in another language
Why wrong: Machine translation is a distinct NLP task — WSD determines word sense within a single language.
- D
Measuring how many distinct meanings a word has across a dictionary
Why wrong: Counting word senses is lexicography — WSD is the task of selecting the correct sense for a specific usage in context.
Quick Answer
The correct answer is that word sense disambiguation (WSD) is the NLP task of determining which meaning of an ambiguous word is intended based on its surrounding context. This is correct because WSD tackles polysemous words—words like "bank" that can mean a financial institution or a river bank—by analyzing syntactic and semantic cues from nearby words to resolve ambiguity. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how Azure AI Language services handle context-dependent meaning, often appearing in questions about text analytics or language understanding challenges. A common trap is confusing WSD with simple part-of-speech tagging; remember that WSD requires deeper semantic reasoning, not just grammatical labels. The core challenge for NLP is that subtle contextual signals and real-world knowledge are needed to disambiguate, which is why even advanced models struggle. Memory tip: think "WSD = Which Sense, Dude?" to recall it’s about picking the right meaning from context.
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 'word sense disambiguation' (WSD) and why is it challenging for NLP?
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
Determining which meaning of an ambiguous word is intended based on surrounding context
Option B is correct because word sense disambiguation (WSD) is the NLP task of identifying which specific meaning of a polysemous word (a word with multiple meanings) is intended in a given context, using surrounding words, syntax, and semantic cues. This is challenging because many words have multiple, often unrelated meanings (e.g., 'bank' as a financial institution vs. river bank), and the correct sense depends on subtle contextual signals that are difficult for models to capture without deep understanding of the domain or world knowledge.
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.
- ✗
Correcting spelling mistakes caused by homophones (words that sound the same)
Why it's wrong here
Homophone correction is spell-checking — WSD determines which sense of a polysemous word is intended in context.
- ✓
Determining which meaning of an ambiguous word is intended based on surrounding context
Why this is correct
WSD resolves lexical ambiguity — understanding 'bank' means financial institution vs. riverbank based on sentence context.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Translating words from one language to their exact equivalent in another language
Why it's wrong here
Machine translation is a distinct NLP task — WSD determines word sense within a single language.
- ✗
Measuring how many distinct meanings a word has across a dictionary
Why it's wrong here
Counting word senses is lexicography — WSD is the task of selecting the correct sense for a specific usage in context.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse WSD with related but distinct NLP tasks like homophone correction (A) or machine translation (C), because all involve handling ambiguous words, but WSD specifically targets meaning selection within a single language based on context.
Detailed technical explanation
How to think about this question
Under the hood, WSD often relies on supervised learning with sense-annotated corpora (e.g., SemCor) or knowledge-based methods using lexical databases like WordNet, where each sense is linked to a synset. A subtle challenge is that many words have fine-grained sense distinctions (e.g., 'bank' has over 10 senses in WordNet), and context windows must be large enough to capture domain-specific cues, yet too large can introduce noise. In Azure AI Language, the 'Entity Linking' feature performs a form of WSD by disambiguating named entities against a knowledge base (e.g., Wikipedia), but general WSD for common nouns remains an active research area due to the lack of large, high-quality sense-annotated datasets.
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: Determining which meaning of an ambiguous word is intended based on surrounding context — Option B is correct because word sense disambiguation (WSD) is the NLP task of identifying which specific meaning of a polysemous word (a word with multiple meanings) is intended in a given context, using surrounding words, syntax, and semantic cues. This is challenging because many words have multiple, often unrelated meanings (e.g., 'bank' as a financial institution vs. river bank), and the correct sense depends on subtle contextual signals that are difficult for models to capture without deep understanding of the domain or world knowledge.
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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