The answer is key phrase extraction. This Azure AI Language feature is designed to automatically identify and extract the main topics, concepts, or frequently mentioned terms—such as 'battery life', 'customer service', or 'screen quality'—from unstructured text like product reviews. By analyzing thousands of reviews in bulk, it surfaces the most salient phrases without requiring manual reading, directly solving the support team’s need for automated topic discovery. On the AI-900 exam, this scenario tests your understanding of prebuilt key phrase extraction for product reviews as a core text analytics capability, often contrasted with sentiment analysis (which detects emotion) or entity recognition (which identifies specific names or locations). A common trap is confusing key phrase extraction with named entity recognition; remember that key phrases are descriptive multi-word topics, not proper nouns. Memory tip: think “Key phrases = Key topics” to quickly recall that this feature pulls out the main discussion points from any text.
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 wants to automatically analyze thousands of product reviews. Their goal is to extract the most frequently mentioned topics (e.g., 'battery life', 'customer service', 'screen quality') without manually reading each review. Which Azure AI Language feature should they use?
Key phrase extraction is designed to identify the main concepts and topics in a body of text, making it ideal for extracting frequently mentioned subjects from product reviews.
B
Sentiment analysis
Why wrong: Sentiment analysis determines the emotional tone (positive, negative, neutral) of the text, not the specific topics being discussed.
C
Language detection
Why wrong: Language detection identifies the language in which the text is written, not the topics or key phrases within it.
D
Entity recognition
Why wrong: Entity recognition extracts pre-defined categories like persons, organizations, and locations, but not general topics or phrases like 'battery life'.
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 Azure AI Language feature because it automatically identifies the main points or topics (e.g., 'battery life', 'customer service', 'screen quality') from unstructured text. This directly meets the requirement to extract frequently mentioned topics from thousands of product reviews without manual reading.
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
Key phrase extraction is designed to identify the main concepts and topics in a body of text, making it ideal for extracting frequently mentioned subjects from product reviews.
Related concept
Read the scenario before looking for a memorised answer.
✗
Sentiment analysis
Why it's wrong here
Sentiment analysis determines the emotional tone (positive, negative, neutral) of the text, not the specific topics being discussed.
✗
Language detection
Why it's wrong here
Language detection identifies the language in which the text is written, not the topics or key phrases within it.
✗
Entity recognition
Why it's wrong here
Entity recognition extracts pre-defined categories like persons, organizations, and locations, but not general topics or phrases like 'battery life'.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'entity recognition' with 'key phrase extraction', but entity recognition only extracts proper nouns (e.g., 'Apple', 'New York'), while key phrase extraction captures descriptive multi-word topics (e.g., 'battery life').
Trap categories for this question
Keyword trap
Language detection identifies the language in which the text is written, not the topics or key phrases within it.
Detailed technical explanation
How to think about this question
Key phrase extraction uses a pre-trained natural language processing model that applies a statistical algorithm to identify and rank phrases that are most relevant to the document's content. Under the hood, it leverages techniques like TF-IDF (Term Frequency-Inverse Document Frequency) and dependency parsing to isolate noun phrases that carry semantic weight. In a real-world scenario, a support team could use this feature to aggregate thousands of reviews and automatically surface the top 10 most discussed product aspects, enabling data-driven product improvements.
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: Key phrase extraction — Key phrase extraction is the correct Azure AI Language feature because it automatically identifies the main points or topics (e.g., 'battery life', 'customer service', 'screen quality') from unstructured text. This directly meets the requirement to extract frequently mentioned topics from thousands of product reviews without manual reading.
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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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A retail company wants to automatically analyze thousands of product reviews to identify the most frequently mentioned aspects, such as 'battery life', 'screen quality', and 'customer service'. They plan to use a prebuilt Azure AI Language feature without any custom training. Which feature should they use?
medium
A.Text Analytics for Health
✓ B.Key phrase extraction
C.Entity linking
D.Sentiment analysis
Why B: Key phrase extraction is the correct choice because it is specifically designed to identify and extract the most important words or phrases from unstructured text, such as product reviews. This prebuilt Azure AI Language feature requires no custom training and directly surfaces frequently mentioned aspects like 'battery life' or 'screen quality' by analyzing term frequency and relevance.
Variation 2. 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?
medium
✓ A.Key phrase extraction
B.Sentiment analysis
C.Entity recognition
D.Language detection
Why A: 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.
Variation 3. A customer support team uses an AI chatbot to analyze incoming messages. They want to automatically identify the most frequently mentioned topics, such as 'shipping delay', 'refund policy', and 'product quality', without manually reading each message. Which Azure AI Language feature should they use?
easy
A.Language Detection
✓ B.Key Phrase Extraction
C.Sentiment Analysis
D.Entity Recognition
Why B: Key Phrase Extraction is the correct choice because it automatically identifies the main topics and concepts in text, such as 'shipping delay', 'refund policy', and 'product quality', without requiring manual reading. This feature returns a list of key phrases that represent the most salient points in the input, making it ideal for topic frequency analysis in customer support messages.
Last reviewed: Jun 11, 2026
Question Discussion
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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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