- A
Entity Recognition
Why wrong: Entity Recognition identifies named entities (people, places, etc.) but does not provide a summary of the document's main points.
- B
Extractive Summarization
Extractive Summarization is designed to create a summary by extracting key sentences, directly meeting the requirement to capture main points.
- C
Key Phrase Extraction
Key Phrase Extraction returns the most important words and phrases, which can highlight key topics and contribute to understanding main points, making it a supporting feature for summarization.
- D
Sentiment Analysis
Why wrong: Sentiment Analysis determines the emotional tone of the text but does not summarize the content.
Question 267 of 993
AI-102 Extractive Summarization Practice Question
This AI-102 practice question tests your understanding of implement natural language processing solutions. 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. A key principle to apply: extractive Summarization. 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.
A company uses Azure AI Language Service to summarize long documents. They need to generate concise summaries that capture the main points. Which feature should they use?
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
✓
Extractive Summarization
Extractive Summarization generates a concise summary by selecting the most important sentences from the document, directly producing a coherent summary. Key Phrase Extraction identifies the main topics and terms, which helps in understanding the main points even though it does not form a summary. Both features are valid for capturing main points: Extractive Summarization provides a readable summary, while Key Phrase Extraction offers a quick overview of key topics. Therefore, both options are correct for this scenario.
Key principle: Extractive Summarization
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Entity Recognition
Why it's wrong here
Entity Recognition identifies named entities (people, places, etc.) but does not provide a summary of the document's main points.
- ✓
Extractive Summarization
Why this is correct
Extractive Summarization is designed to create a summary by extracting key sentences, directly meeting the requirement to capture main points.
Related concept
Extractive Summarization
- ✓
Key Phrase Extraction
Why this is correct
Key Phrase Extraction returns the most important words and phrases, which can highlight key topics and contribute to understanding main points, making it a supporting feature for summarization.
Related concept
Extractive Summarization
- ✗
Sentiment Analysis
Why it's wrong here
Sentiment Analysis determines the emotional tone of the text but does not summarize the content.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that only Extractive Summarization (or only Key Phrase Extraction) is suitable. In reality, both features can help capture main points: Extractive Summarization delivers a full summary, whereas Key Phrase Extraction returns key terms that represent core topics. Candidates may overlook Key Phrase Extraction as a valid option because it does not produce a narrative summary.
Detailed technical explanation
How to think about this question
Extractive Summarization works by scoring each sentence in the document based on features like term frequency, sentence position, and similarity to the document's overall content, then selecting the top-ranked sentences to form the summary. The Azure AI Language Service provides a rank score for each extracted sentence, allowing you to control summary length by specifying a maximum number of sentences or a percentage of the original text. In a real-world scenario, this is ideal for legal document review where you need to quickly grasp the key clauses without reading hundreds of pages.
KKey Concepts to Remember
- Extractive Summarization
- Key Phrase Extraction
- Abstractive Summarization
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
Extractive Summarization
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. Extractive Summarization 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
Got this wrong? Here's your next step.
Review extractive Summarization, then practise related AI-102 questions on the same topic to reinforce the concept.
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Implement natural language processing solutions — study guide chapter
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement natural language processing solutions — This question tests Implement natural language processing solutions — Extractive Summarization.
What is the correct answer to this question?
The correct answer is: Extractive Summarization — Extractive Summarization generates a concise summary by selecting the most important sentences from the document, directly producing a coherent summary. Key Phrase Extraction identifies the main topics and terms, which helps in understanding the main points even though it does not form a summary. Both features are valid for capturing main points: Extractive Summarization provides a readable summary, while Key Phrase Extraction offers a quick overview of key topics. Therefore, both options are correct for this scenario.
What should I do if I get this AI-102 question wrong?
Review extractive Summarization, then practise related AI-102 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Extractive Summarization
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Last reviewed: Jul 4, 2026
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