- A
Sentiment Analysis
Why wrong: Sentiment analysis detects sentiment, not key phrases.
- B
Language Detection
Why wrong: Language detection identifies the language.
- C
Key Phrase Extraction
Key Phrase Extraction extracts key phrases from text.
- D
Named Entity Recognition
Why wrong: NER identifies entities like names and dates.
Question 740 of 993
AI-102 Practice Question: Implement natural language processing solutions
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.
You are building a solution to extract key phrases from customer reviews using Azure AI Language. Which feature should you 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 feature because it is specifically designed to identify and return the main talking points or important terms from unstructured text, such as customer reviews. Azure AI Language's Key Phrase Extraction API analyzes the text structure and linguistic patterns to surface the most relevant phrases, which directly addresses the requirement to extract key phrases from 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.
- ✗
Sentiment Analysis
Why it's wrong here
Sentiment analysis detects sentiment, not key phrases.
- ✗
Language Detection
Why it's wrong here
Language detection identifies the language.
- ✓
Key Phrase Extraction
Why this is correct
Key Phrase Extraction extracts key phrases from text.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Named Entity Recognition
Why it's wrong here
NER identifies entities like names and dates.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Named Entity Recognition with Key Phrase Extraction, because both involve identifying important words, but NER is strictly for predefined entity types (e.g., person, location) while Key Phrase Extraction captures any salient topic or concept from the text.
Trap categories for this question
Keyword trap
Sentiment analysis detects sentiment, not key phrases.
Detailed technical explanation
How to think about this question
Key Phrase Extraction in Azure AI Language uses a statistical model based on a large corpus of text to identify words and phrases that are most likely to represent the main topics. The API returns a list of strings ranked by relevance, and it can handle multiple languages via the language parameter. In a real-world scenario, for a review like 'The camera quality is amazing but the battery life is poor,' Key Phrase Extraction would return ['camera quality', 'battery life'], while Sentiment Analysis would return a mixed sentiment score.
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
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement natural language processing solutions — This question tests Implement natural language processing solutions — 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 feature because it is specifically designed to identify and return the main talking points or important terms from unstructured text, such as customer reviews. Azure AI Language's Key Phrase Extraction API analyzes the text structure and linguistic patterns to surface the most relevant phrases, which directly addresses the requirement to extract key phrases from reviews.
What should I do if I get this AI-102 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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Last reviewed: Jul 4, 2026
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