AI-102 Practice Question: Implement natural language processing solutions
A team is developing a solution to automatically summarize long documents using Azure AI Language. Which feature should they use?
⚠ Common exam trap
Candidates often confuse key phrase extraction (which finds important words) with extractive summarization (which extracts entire sentences), leading them to choose Option B instead of the correct feature for document summarization.
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 is the correct feature because it specifically identifies and extracts the most important sentences from a document to create a concise summary. Azure AI Language's extractive summarization uses a ranking model to score sentences based on relevance and informativeness, directly addressing the requirement to automatically summarize long documents.
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
Determines sentiment, not summary.
- ✗
Key phrase extraction.
Why it's wrong here
Extracts key phrases, not a coherent summary.
- ✓
Extractive summarization.
Why this is correct
Extracts key sentences to create a summary.
- ✗
Entity recognition.
Why it's wrong here
Identifies entities, not summary.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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