Quick Answer
The answer is Key Phrase Extraction, as this Azure AI Language feature is specifically designed to identify and return the main points, topics, and important terms from unstructured text like customer reviews. Technically, Key Phrase Extraction uses a pre-trained natural language processing model to analyze the syntactic structure and semantic relationships within the text, surfacing the most salient phrases that summarize the document’s core content. On the AI-102 exam, this question tests your ability to distinguish between the four primary Language service features: Key Phrase Extraction for summarizing topics, Entity Recognition for identifying named items like people or places, Sentiment Analysis for gauging opinion, and Language Detection for identifying the script. A common trap is confusing Key Phrase Extraction with Entity Recognition, but remember that entities are specific, predefined categories (e.g., “Microsoft” as an organization), while key phrases are broader, descriptive topics (e.g., “excellent battery life”). For a quick memory tip, think “Key Phrases = Key Points,” and if you need to find the “what” of a review, not the “who” or “how it feels,” Key Phrase Extraction is your tool.
This AI-102 practice question tests your understanding of implement natural language processing solutions. 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.
Your application needs to extract key phrases from customer reviews to identify common topics. Which Azure AI Language feature should you use?
Sentiment Analysis
Why wrong: Sentiment Analysis detects positive/negative tone, not topics.
Language Detection
Why wrong: Language Detection identifies the language of the text.
Named Entity Recognition
Why wrong: NER extracts specific entities like people, places, not general topics.
Key Phrase Extraction
Key Phrase Extraction identifies main talking points.
Answer choices
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 feature for extracting key points from text. Entity Recognition extracts named entities. Sentiment Analysis determines sentiment. Language Detection identifies language.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Sentiment Analysis
Why it's wrong here
Sentiment Analysis detects positive/negative tone, not topics.
Language Detection
Why it's wrong here
Language Detection identifies the language of the text.
Named Entity Recognition
Why it's wrong here
NER extracts specific entities like people, places, not general topics.
Key Phrase Extraction
Why this is correct
Key Phrase Extraction identifies main talking points.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Implement natural language processing solutions — This question tests Implement natural language processing solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Key Phrase Extraction — Key Phrase Extraction is the correct feature for extracting key points from text. Entity Recognition extracts named entities. Sentiment Analysis determines sentiment. Language Detection identifies language.
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
This AI-102 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-102 exam.
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