Describe features of Natural Language Processing workloads on Azure →easyMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
A company analyzes customer reviews to identify common themes. They need to automatically extract the most important concepts from each review, such as 'battery life', 'customer service', and 'price'. Which Azure AI Language feature should they use?
⚠ Common exam trap
Test-takers frequently confuse named entity recognition (NER) with key phrase extraction, assuming NER can extract any important concept, but NER is limited to predefined categories like persons, locations, and organizations, not general multi-word phrases like 'battery life'.
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
✓
Key phrase extraction
Key phrase extraction is the correct Azure AI Language feature because it automatically identifies and extracts the most important concepts, such as 'battery life', 'customer service', and 'price', from unstructured text like customer reviews. It returns a list of key phrases that represent the main topics discussed, which directly matches the requirement to extract important concepts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Sentiment analysis
Why it's wrong here
Sentiment analysis classifies text into polarity categories such as positive, negative, or neutral, often using a numeric score. It measures opinion or emotion rather than extracting topical content, so it cannot surface the recurring concepts or phrases that represent common themes. Unlike key phrase extraction, it returns a classification or score, not a list of relevant terms from the text.
- ✓
Key phrase extraction
Why this is correct
Key phrase extraction uses natural language processing to identify the most salient terms and multi-word expressions within each review, such as 'battery life,' 'customer service,' or 'shipping speed.' This matches the company's goal directly because the returned phrases reveal which themes recur most frequently across the corpus. In Azure Text Analytics, this operation returns an ordered list of key phrases per document, enabling frequency-based theme aggregation.
- ✗
Language detection
Why it's wrong here
Language detection determines which natural language a document is written in by analyzing character patterns and word frequencies, returning a language code and confidence score. It does not inspect semantic content, so it cannot identify themes or important concepts even if the language is known. For this scenario, detecting the language would only be a preprocessing step, not the analytical task that surfaces common topics from the reviews.
- ✗
Named entity recognition
Why it's wrong here
Named entity recognition extracts typed, structured references to people, organizations, locations, dates, and similar categories, mapping them to predetermined entity types. It does not identify descriptive multi-word concepts or themes, so a mention like 'John' or 'Seattle' would be captured, but a common theme such as 'ease of installation' would not. Because the company needs recurring idea-level commonalities, not discrete named items, this service is not a fit.
Go deeper
Related to this question
Learn chapter
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
Key phrase extraction
Key phrase extraction is an Azure AI service feature that automatically identifies and extracts the most important words and phrases from a piece of text.
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