- A
Keyword extraction returns single words; key phrase extraction returns multi-word phrases
Why wrong: In Azure AI Language, these are effectively the same capability — 'key phrase extraction' is the official name and it returns important concepts which may be single or multi-word.
- B
Both terms refer to the same Azure AI Language feature that extracts important concept phrases from text
Key phrase extraction (the official name) identifies important single or multi-word concepts — 'keyword extraction' is an informal synonym.
- C
Keyword extraction is a legacy feature; key phrase extraction is the new replacement
Why wrong: No legacy/new distinction exists — key phrase extraction is the consistent name in Azure AI Language.
- D
Key phrase extraction requires custom training; keyword extraction uses pre-built models
Why wrong: Both are pre-built capabilities requiring no training — the distinction is naming, not training requirements.
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 'keyword extraction' vs 'key phrase extraction' in Azure AI Language?
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
Both terms refer to the same Azure AI Language feature that extracts important concept phrases from text
Option B is correct because in Azure AI Language, 'key phrase extraction' is the official feature name that identifies the main concepts in a text, and 'keyword extraction' is an informal term sometimes used interchangeably. The service does not distinguish between single-word and multi-word extraction as separate features; it returns a list of key phrases that can be single words or multi-word expressions based on the text's context.
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.
- ✗
Keyword extraction returns single words; key phrase extraction returns multi-word phrases
Why it's wrong here
In Azure AI Language, these are effectively the same capability — 'key phrase extraction' is the official name and it returns important concepts which may be single or multi-word.
- ✓
Both terms refer to the same Azure AI Language feature that extracts important concept phrases from text
Why this is correct
Key phrase extraction (the official name) identifies important single or multi-word concepts — 'keyword extraction' is an informal synonym.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Keyword extraction is a legacy feature; key phrase extraction is the new replacement
Why it's wrong here
No legacy/new distinction exists — key phrase extraction is the consistent name in Azure AI Language.
- ✗
Key phrase extraction requires custom training; keyword extraction uses pre-built models
Why it's wrong here
Both are pre-built capabilities requiring no training — the distinction is naming, not training requirements.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume 'keyword' and 'key phrase' are distinct features based on word count, but Azure AI Language treats them as the same feature, and the exam tests this exact terminology confusion.
Trap categories for this question
Keyword trap
In Azure AI Language, these are effectively the same capability — 'key phrase extraction' is the official name and it returns important concepts which may be single or multi-word.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Language's key phrase extraction leverages a transformer-based model that scores candidate phrases by relevance, returning the top N phrases (default 3) sorted by confidence. A subtle behavior is that the service can return overlapping phrases (e.g., 'machine learning' and 'deep learning') if both are contextually significant, and it supports multiple languages via language-specific models. In a real-world scenario, analyzing customer feedback might yield phrases like 'slow service' and 'long wait times' as key phrases, not individual keywords.
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: Both terms refer to the same Azure AI Language feature that extracts important concept phrases from text — Option B is correct because in Azure AI Language, 'key phrase extraction' is the official feature name that identifies the main concepts in a text, and 'keyword extraction' is an informal term sometimes used interchangeably. The service does not distinguish between single-word and multi-word extraction as separate features; it returns a list of key phrases that can be single words or multi-word expressions based on the text's context.
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.
About these practice questions
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Last reviewed: Jun 11, 2026
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