The answer is extractive summarization, the prebuilt Azure AI Language feature designed to condense long documents into key sentences by pulling the most important statements directly from the original text. Unlike abstractive summarization, which generates new phrasing, extractive summarization works by scoring each sentence for relevance and then selecting the top-scoring ones, making it ideal for distilling hundreds of product reviews into concise, readable takeaways without altering the original wording. On the AI-900 exam, this question tests your ability to distinguish between the two summarization types: extractive (selects existing sentences) versus abstractive (generates new text). A common trap is confusing these two or assuming any “summarization” feature works the same way, so remember that “extractive” literally extracts—think of it as a highlighter picking the best lines from a page. For a quick memory tip, associate “extractive” with “extract” and “abstractive” with “abstract painting”—one pulls real pieces, the other creates something new.
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 customer support team receives hundreds of long product reviews every day. They want to automatically summarize each review into a few key sentences to quickly understand the main points. Which prebuilt Azure AI Language feature should they use?
Why wrong: Key phrase extraction returns a list of important words and phrases from the document, but it does not create a coherent summary consisting of full sentences.
B
Sentiment analysis
Why wrong: Sentiment analysis determines whether the overall sentiment is positive, negative, or neutral. It does not produce a summary of the content.
C
Extractive summarization
Extractive summarization selects the most representative sentences from the original text and arranges them to form a summary. It is a prebuilt feature in Azure AI Language.
D
Entity recognition
Why wrong: Entity recognition identifies and categorizes named entities such as people, organizations, and locations. It does not summarize the document.
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 choice because it is specifically designed to condense long documents into a few key sentences by extracting the most important sentences directly from the original text. This aligns perfectly with the customer support team's goal of automatically summarizing hundreds of product reviews into concise, key points for quick understanding.
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.
✗
Key phrase extraction
Why it's wrong here
Key phrase extraction returns a list of important words and phrases from the document, but it does not create a coherent summary consisting of full sentences.
✗
Sentiment analysis
Why it's wrong here
Sentiment analysis determines whether the overall sentiment is positive, negative, or neutral. It does not produce a summary of the content.
✓
Extractive summarization
Why this is correct
Extractive summarization selects the most representative sentences from the original text and arranges them to form a summary. It is a prebuilt feature in Azure AI Language.
Related concept
Read the scenario before looking for a memorised answer.
✗
Entity recognition
Why it's wrong here
Entity recognition identifies and categorizes named entities such as people, organizations, and locations. It does not summarize the document.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse key phrase extraction with summarization, assuming that extracting key phrases is sufficient to summarize a review, but key phrases are not sentences and cannot convey the main points in a readable, coherent form.
Trap categories for this question
Keyword trap
Key phrase extraction returns a list of important words and phrases from the document, but it does not create a coherent summary consisting of full sentences.
Detailed technical explanation
How to think about this question
Extractive summarization in Azure AI Language uses a transformer-based model (e.g., BERT) to score each sentence in the input text based on relevance, then selects the top-ranked sentences to form the summary. The feature supports both single-document and multi-document summarization, and the output can be controlled by specifying the maximum number of sentences or a percentage of the original text. In a real-world scenario, this is ideal for processing a batch of product reviews daily, as it can be integrated into an automated pipeline using the Azure AI Language REST API or SDK.
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: Extractive summarization — Extractive summarization is the correct choice because it is specifically designed to condense long documents into a few key sentences by extracting the most important sentences directly from the original text. This aligns perfectly with the customer support team's goal of automatically summarizing hundreds of product reviews into concise, key points for quick understanding.
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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