AI-102 Practice Question: Implement natural language processing solutions
A company wants to use Azure AI Language to automatically summarize large documents. The summarization must extract the most important sentences from each document. Which feature should they use?
⚠ Common exam trap
Test-takers frequently confuse 'key phrase extraction' with summarization because both involve identifying important content, but key phrase extraction returns only isolated terms, not coherent sentences, which fails the requirement for a sentence-based summary.
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 selects the most important sentences directly from the source document to create a concise summary, preserving the original wording. This aligns with the requirement to extract key sentences without generating new text, making it the correct choice for this scenario.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Extractive summarization
Why this is correct
Extractive summarization returns the highest-scoring sentences verbatim from the source document, directly satisfying the requirement to pull the most important sentences rather than generate new phrasing. Abstractive summarization would instead rewrite content in fresh wording, which the stem explicitly excludes.
- ✗
Abstractive summarization
Why it's wrong here
Abstractive summarization generates new paraphrased sentences rather than extracting the most important existing sentences from the document. It is tempting because it is a genuine summarization feature, and it would be correct when the requirement is a concise rewritten summary instead of verbatim sentence extraction.
- ✗
Key phrase extraction
Why it's wrong here
Key phrase extraction returns salient terms and phrases, not whole sentences, so it cannot produce sentence-level summaries. It is tempting because it also mines documents for important content, and it would be correct when the requirement is tagging or indexing key topics rather than condensing text into sentences.
- ✗
Entity recognition
Why it's wrong here
Entity recognition labels people, places, organisations and similar items within text; it never selects or outputs summary sentences. It is tempting because it also analyses document content, and it would be the correct choice when the requirement is extracting structured entities such as names, dates or locations for downstream processing.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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