- A
Document summarization
Document summarization provides a concise summary of the document.
- B
Key phrase extraction
Why wrong: Key phrase extraction extracts key phrases, not a summary.
- C
Entity recognition
Why wrong: Entity recognition identifies named entities.
- D
Sentiment analysis
Why wrong: Sentiment analysis determines the sentiment of the text.
Question 348 of 993
Document Summarization with Azure AI Language
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. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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.
You need to summarize a large document using Azure AI Language. Which feature should you 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
✓
Document summarization
Document summarization is the correct feature because it is specifically designed to generate concise summaries of large documents, extracting the most important information. Azure AI Language's document summarization uses extractive or abstractive techniques to produce a summary, directly addressing the requirement to summarize a large document.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Document summarization
Why this is correct
Document summarization provides a concise summary of the document.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Key phrase extraction
Why it's wrong here
Key phrase extraction extracts key phrases, not a summary.
- ✗
Entity recognition
Why it's wrong here
Entity recognition identifies named entities.
- ✗
Sentiment analysis
Why it's wrong here
Sentiment analysis determines the sentiment of the text.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse key phrase extraction with summarization, thinking that extracting important phrases is equivalent to summarizing the document, but key phrase extraction lacks the narrative structure and coherence of a true summary.
Trap categories for this question
Keyword trap
Key phrase extraction extracts key phrases, not a summary.
Detailed technical explanation
How to think about this question
Azure AI Language's document summarization can operate in extractive mode, which selects key sentences from the original text, or abstractive mode, which generates new sentences that capture the essence. The feature uses transformer-based models fine-tuned on summarization tasks, and it can handle documents up to 125,000 characters. In a real-world scenario, this is used to automatically generate executive summaries of lengthy legal contracts or research papers.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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.
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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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Document summarization — Document summarization is the correct feature because it is specifically designed to generate concise summaries of large documents, extracting the most important information. Azure AI Language's document summarization uses extractive or abstractive techniques to produce a summary, directly addressing the requirement to summarize a large document.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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