AI-102 Practice Question: Implement natural language processing solutions
Your company uses Azure AI Language to analyze customer feedback. You need to extract key phrases from reviews in multiple languages. Which feature should you use?
⚠ Common exam trap
Test-takers frequently confuse key phrase extraction with named entity recognition, assuming that extracting important names or places is the same as extracting key topics, but NER focuses on specific entity types while key phrase extraction captures broader, contextually important phrases.
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 extract the most important points or topics from text, regardless of the language. Azure AI Language's key phrase extraction supports multiple languages and returns a list of key phrases that represent the main subjects discussed in the customer feedback, which directly meets the requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Named entity recognition
Why it's wrong here
Named entity recognition labels spans as people, places, organisations, or dates; it does not return the salient noun phrases key phrase extraction produces. It is tempting because it also parses multilingual text, but its output is typed entities, not the untyped key phrases the stem demands.
- ✗
Language detection
Why it's wrong here
Language detection only returns the detected language name and ISO code for each document; it extracts no phrases at all. It is tempting because the stem mentions multiple languages, but detection is a prerequisite step, not the extraction feature itself — key phrase extraction handles multilingual input directly.
- ✓
Key phrase extraction
Why this is correct
Key phrase extraction in Azure AI Language identifies the main concepts in text and natively supports multiple languages, satisfying the multilingual reviews constraint. It returns salient terms directly from each document, so no translation pipeline is needed before analysis.
- ✗
Sentiment analysis
Why it's wrong here
Sentiment analysis returns positive, negative, or neutral scores plus confidence values; it never surfaces the noun phrases the stem requires. It is tempting because it also processes multilingual review text, but it answers a different question — gauging opinion polarity rather than extracting key phrases.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.