- A
Key phrase extraction
Correct because key phrase extraction automatically identifies the most important words and phrases that summarize the main topics discussed in a document. It directly answers the need to find commonly discussed aspects like 'price' and 'durability'.
- B
Sentiment analysis
Why wrong: Incorrect because sentiment analysis determines whether a text is positive, negative, or neutral. It does not extract the specific topics being discussed.
- C
Entity recognition
Why wrong: Incorrect because entity recognition identifies and categorizes named entities like persons, locations, organizations, and dates. It does not extract general contextual topics like 'durability'.
- D
Language detection
Why wrong: Incorrect because language detection identifies the language in which a text is written. It does not provide any insight into the content or topics.
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 marketing team wants to automatically analyze thousands of customer reviews to identify the most commonly discussed aspects, such as 'price', 'durability', or 'customer service'. They do not have any labeled data for custom training. Which prebuilt Azure AI Language feature should they 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
Key phrase extraction
Key phrase extraction is the correct choice because it automatically identifies the most important points or topics (like 'price', 'durability', 'customer service') from unstructured text without requiring any labeled training data. This prebuilt Azure AI Language feature is designed specifically to surface commonly discussed aspects from large volumes of text, making it ideal for analyzing thousands of customer reviews.
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 this is correct
Correct because key phrase extraction automatically identifies the most important words and phrases that summarize the main topics discussed in a document. It directly answers the need to find commonly discussed aspects like 'price' and 'durability'.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Sentiment analysis
Why it's wrong here
Incorrect because sentiment analysis determines whether a text is positive, negative, or neutral. It does not extract the specific topics being discussed.
- ✗
Entity recognition
Why it's wrong here
Incorrect because entity recognition identifies and categorizes named entities like persons, locations, organizations, and dates. It does not extract general contextual topics like 'durability'.
- ✗
Language detection
Why it's wrong here
Incorrect because language detection identifies the language in which a text is written. It does not provide any insight into the content or topics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'key phrase extraction' with 'entity recognition', mistakenly thinking that named entities like 'price' or 'customer service' are entities, when in fact they are general concepts extracted as key phrases, not predefined entity categories.
Detailed technical explanation
How to think about this question
Key phrase extraction in Azure AI Language uses a statistical natural language processing model that identifies noun phrases and other salient terms by analyzing term frequency, co-occurrence patterns, and part-of-speech tagging. Under the hood, it leverages a multi-layer neural network trained on a large corpus of general web text, which allows it to surface relevant phrases without any domain-specific fine-tuning. In a real-world scenario, a marketing team could use this feature to automatically generate a word cloud or frequency table of the top discussed aspects from thousands of reviews, enabling rapid identification of customer priorities.
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-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: Key phrase extraction — Key phrase extraction is the correct choice because it automatically identifies the most important points or topics (like 'price', 'durability', 'customer service') from unstructured text without requiring any labeled training data. This prebuilt Azure AI Language feature is designed specifically to surface commonly discussed aspects from large volumes of text, making it ideal for analyzing thousands of customer reviews.
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.
About these practice questions
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Last reviewed: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.
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