The correct answer is extractive summarization. This Azure AI Language feature directly fulfills the requirement because it analyzes a document and extracts the most salient sentences, ranked by relevance, rather than generating new text. For headline generation, where the goal is to select a single, existing sentence from the article, extractive summarization is the precise tool, as it returns complete sentences rather than isolated keywords or phrases. On the AI-900 exam, this question tests your ability to distinguish between Azure’s text analytics capabilities; a common trap is confusing it with key phrase extraction, which only yields short terms. Remember, headlines need complete thoughts, not just keywords. A useful memory tip: think of “extractive” as “extracting” a ready-made sentence, while “abstractive” would write a new one—Azure’s feature here is purely extractive.
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
This AI-900 practice question tests your understanding of describe features of natural language processing workloads on azure. 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.
A news agency wants to build a system that can automatically generate a short headline for each news article. The system should select the most important sentence from the article as the headline. Which Azure AI Language feature should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
B: Extractive summarization
Extractive summarization is the correct Azure AI Language feature because it identifies and extracts the most important sentences from a document, which directly matches the requirement to select the most important sentence as a headline. Unlike key phrase extraction, which returns individual words or short phrases, extractive summarization returns complete sentences ranked by relevance, making it ideal for headline generation.
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.
✗
A: Key phrase extraction
Why it's wrong here
Key phrase extraction returns key terms, not full sentences suitable as headlines.
✓
B: Extractive summarization
Why this is correct
Correct: Extractive summarization selects the most important sentences from the text.
Related concept
Read the scenario before looking for a memorised answer.
✗
C: Entity recognition
Why it's wrong here
Entity recognition extracts specific entities (e.g., names, dates), not whole sentences.
✗
D: Sentiment analysis
Why it's wrong here
Sentiment analysis detects positive/negative tone, not key sentences.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse key phrase extraction with extractive summarization, assuming that extracting 'key phrases' is the same as extracting sentences, but key phrase extraction yields only isolated words or short phrases, not complete, coherent sentences suitable for a headline.
Trap categories for this question
Keyword trap
Key phrase extraction returns key terms, not full sentences suitable as headlines.
Detailed technical explanation
How to think about this question
Extractive summarization in Azure AI Language uses a ranking algorithm based on features such as sentence position, term frequency, and semantic similarity to the document's overall meaning. The service returns a confidence score for each extracted sentence, allowing the system to pick the top-ranked sentence as the headline. In practice, this feature can handle articles up to 5,120 characters and returns up to 20 sentences, but for headline generation, only the highest-scoring sentence is typically used.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of Natural Language Processing workloads on Azure — This question tests Describe features of Natural Language Processing workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: B: Extractive summarization — Extractive summarization is the correct Azure AI Language feature because it identifies and extracts the most important sentences from a document, which directly matches the requirement to select the most important sentence as a headline. Unlike key phrase extraction, which returns individual words or short phrases, extractive summarization returns complete sentences ranked by relevance, making it ideal for headline generation.
What should I do if I get this AI-900 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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Question Discussion
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