Question 595 of 1,020

Describe features of Natural Language Processing workloads on Azure →mediumMultiple ChoiceObjective-mapped

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.

What is the difference between extractive summarization and abstractive summarization?

A
Extractive works on text; abstractive works on images
Why wrong: Both summarization types operate on text — the difference is in how they produce the summary.
B
Extractive pulls existing sentences; abstractive generates new text capturing the meaning
Extractive = copy key sentences from original; abstractive = generate new condensed sentences that paraphrase the content.
C
Extractive is for long documents; abstractive is for short text
Why wrong: Both types can be applied to various text lengths — the distinction is in output generation method.
D
Extractive summarization is always less accurate than abstractive
Why wrong: Accuracy depends on context — extractive is more faithful to source text; abstractive may paraphrase more fluidly but introduces interpretation.

Full breakdown with real-world context →

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 pulls existing sentences; abstractive generates new text capturing the meaning

Option B is correct because extractive summarization identifies and extracts the most important sentences directly from the source text, while abstractive summarization generates new sentences that capture the core meaning, often using natural language generation techniques. In Azure AI Language, extractive summarization returns a set of ranked sentences from the original document, whereas abstractive summarization produces a concise summary that may rephrase content. This distinction is fundamental to understanding how different NLP workloads handle text summarization tasks.

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.

✗
Extractive works on text; abstractive works on images
Why it's wrong here
Both summarization types operate on text — the difference is in how they produce the summary.
✓
Extractive pulls existing sentences; abstractive generates new text capturing the meaning
Why this is correct
Extractive = copy key sentences from original; abstractive = generate new condensed sentences that paraphrase the content.
Related concept
Read the scenario before looking for a memorised answer.
✗
Extractive is for long documents; abstractive is for short text
Why it's wrong here
Both types can be applied to various text lengths — the distinction is in output generation method.
✗
Extractive summarization is always less accurate than abstractive
Why it's wrong here
Accuracy depends on context — extractive is more faithful to source text; abstractive may paraphrase more fluidly but introduces interpretation.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates confuse the terms 'extractive' and 'abstractive' with other AI workloads (like image processing) or assume one is always superior, when in fact the key difference is whether the summary uses existing sentences or generates new text.

Trap categories for this question

Keyword trap
Accuracy depends on context — extractive is more faithful to source text; abstractive may paraphrase more fluidly but introduces interpretation.
Command / output trap
Both types can be applied to various text lengths — the distinction is in output generation method.

Detailed technical explanation

How to think about this question

Under the hood, extractive summarization uses sentence scoring algorithms (e.g., TextRank or BERT-based models) to rank sentences by relevance and then selects the top-k sentences. Abstractive summarization leverages sequence-to-sequence models (e.g., T5 or BART) that encode the input text and decode a new, shorter representation, which requires handling paraphrasing and content compression. In Azure, the extractive summarization API returns a confidence score per sentence, while abstractive summarization returns a single generated summary string, making the choice critical for applications requiring verbatim accuracy versus fluent condensation.

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.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this AI-900 question test?

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 pulls existing sentences; abstractive generates new text capturing the meaning — Option B is correct because extractive summarization identifies and extracts the most important sentences directly from the source text, while abstractive summarization generates new sentences that capture the core meaning, often using natural language generation techniques. In Azure AI Language, extractive summarization returns a set of ranked sentences from the original document, whereas abstractive summarization produces a concise summary that may rephrase content. This distinction is fundamental to understanding how different NLP workloads handle text summarization tasks.

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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