Describe features of Natural Language Processing workloads on Azure →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
What is 'keyword extraction' vs 'key phrase extraction' in Azure AI Language?
⚠ Common exam trap
Candidates often 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.
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
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.
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
Both names describe the same API operation, so they cannot have different return types. Azure's key phrase extraction returns key phrases that may be single tokens like 'cloud' or multi-word phrases like 'machine learning model,' depending on what the pre-built model identifies as salient. Because 'keyword extraction' is just a synonym for that operation, it has no separate output scope limited to single words. Artificially separating output granularity creates a false distinction not present in the service's actual behavior.
- ✓
Both terms refer to the same Azure AI Language feature that extracts important concept phrases from text
Why this is correct
In Azure AI Language, key phrase extraction is the official feature name, and it returns the most important concepts in a text, which can be either single words or longer expressions. The term 'keyword extraction' is widely used in casual conversation and by some third-party references, but Microsoft's SDKs and documentation consistently call it key phrase extraction. Both names refer to the exact same pre-built endpoint, with the same input parameters and response format. No functional difference exists between the two terms.
- ✗
Keyword extraction is a legacy feature; key phrase extraction is the new replacement
Why it's wrong here
The Azure AI Language service has never used 'keyword extraction' as a separate legacy API, and key phrase extraction is not a replacement for a deprecated feature. Microsoft's current documentation and REST API stable versions use key phrase extraction consistently, with no migration guides for an older keyword-based capability. If keyword extraction were a legacy feature, there would be versioned endpoints or retirement notices, but none exist. Therefore, this option misrepresents the service's history and naming evolution.
- ✗
Key phrase extraction requires custom training; keyword extraction uses pre-built models
Why it's wrong here
Key phrase extraction in Azure AI Language is a pre-built, no-training-required capability; the same REST API handles it without any custom model. Microsoft's documentation does not define a separate 'keyword extraction' feature that uses pre-built models requiring custom training. In fact, 'keyword extraction' is not a distinct Azure service at all—it is just informal wording for the same key phrase extraction operation. Thus, training requirements cannot be used to differentiate them.
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Machine Learning Core Concepts
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
Key term
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
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