AI-900 Key phrase extraction Practice Question
A language teacher uses Azure AI Language to automatically analyze hundreds of student essays. The teacher wants to identify the main topics discussed in each essay and also understand the overall sentiment (positive, negative, or neutral) expressed. Which two prebuilt Azure AI Language features should the teacher use together to accomplish this goal?
⚠ Common exam trap
The trap is assuming entity recognition can identify main topics, but it only identifies specific named entities. The key is recognizing that key phrase extraction is the correct feature for extracting main topics.
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 and Sentiment analysis
The teacher needs two features: one to identify main topics and one to analyze sentiment. Key phrase extraction extracts key phrases that represent the main topics, and sentiment analysis provides the overall sentiment. Option A is the only correct combination. Option D includes entity recognition, which identifies named entities like people or places, not the main topics discussed. Options B and C do not correctly pair a topic-identifying feature with sentiment analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Key phrase extraction and Sentiment analysis
Why this is correct
Correct. Key phrase extraction identifies main topics; sentiment analysis identifies sentiment.
- ✗
Entity recognition and Language detection
Why it's wrong here
Incorrect. Language detection does not identify topics or sentiment.
When this WOULD be correct
A scenario where a multilingual teacher wants to automatically detect the language of each essay and then extract named entities (e.g., student names, locations) for categorization, without needing topic or sentiment analysis.
- ✗
Text summarization and Key phrase extraction
Why it's wrong here
Incorrect. Text summarization produces a summary but does not directly extract main topics; key phrase extraction alone is not paired with sentiment analysis here.
When this WOULD be correct
A question asks: 'A teacher wants to generate a short summary of each essay and also extract the most important words or phrases. Which two features should be used?' Then Text summarization and Key phrase extraction would be correct.
- ✗
Sentiment analysis and Entity recognition
Why it's wrong here
Incorrect. Entity recognition identifies named entities, not main topics; sentiment analysis alone does not cover topics.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Key phrase extraction and Sentiment analysisCorrect answer▾
Why this is correct
Correct. Key phrase extraction identifies main topics; sentiment analysis identifies sentiment.
✗Entity recognition and Language detectionWrong answer — click to see why▾
Why this is wrong here
Language detection identifies the language of text, not topics or sentiment. Entity recognition extracts named entities (e.g., people, places), not main topics or overall sentiment, so it does not meet the teacher's needs.
★ When this WOULD be the correct answer
A scenario where a multilingual teacher wants to automatically detect the language of each essay and then extract named entities (e.g., student names, locations) for categorization, without needing topic or sentiment analysis.
Why candidates choose this
Candidates may confuse entity recognition with key phrase extraction, thinking entities represent main topics, and overlook that language detection is irrelevant for topic and sentiment analysis.
✗Text summarization and Key phrase extractionWrong answer — click to see why▾
Why this is wrong here
Text summarization produces a condensed version of the text, not a list of main topics. Key phrase extraction identifies important phrases but does not analyze sentiment, so the pair fails to meet the requirement for sentiment analysis.
★ When this WOULD be the correct answer
A question asks: 'A teacher wants to generate a short summary of each essay and also extract the most important words or phrases. Which two features should be used?' Then Text summarization and Key phrase extraction would be correct.
Why candidates choose this
Candidates may confuse 'main topics' with 'summary' and think key phrase extraction covers topic identification, overlooking that sentiment analysis is explicitly required for understanding overall sentiment.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Conversational Language Understanding (CLU)
Key term
Azure AI Language
Azure AI Language is a cloud-based service from Microsoft that uses natural language processing to understand, analyze, and generate human language for applications.
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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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.