AI-102 Practice Question: Implement natural language processing solutions
A company wants to analyze customer reviews to determine whether sentiment is positive, negative, or neutral. The solution must also extract key phrases such as 'great battery life' and 'poor camera quality'. Which Azure AI feature should be used?
⚠ Common exam trap
Many exam-takers confuse Named Entity Recognition (NER) with key phrase extraction because both extract text, but NER targets predefined entity types (e.g., person, location) while key phrase extraction targets descriptive phrases that are not entities.
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
✓
Azure AI Language - Sentiment Analysis and Key Phrase Extraction
Azure AI Language's Sentiment Analysis and Key Phrase Extraction are specifically designed to evaluate text for positive, negative, or neutral sentiment and to extract meaningful phrases like 'great battery life' or 'poor camera quality'. This combined capability directly matches the dual requirement of sentiment classification and key phrase extraction in a single API call, using pre-built models that require no custom training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure AI Language - Named Entity Recognition (NER)
Why it's wrong here
NER identifies and classifies entities such as people, places and organisations; it does not produce sentiment labels or key phrases. Azure AI Language's sentiment analysis and key phrase extraction features cover both requirements. NER would be correct when extracting structured entity types from unstructured text.
- ✗
Azure AI Content Safety
Why it's wrong here
Content Safety classifies harmful text and images across categories such as violence or hate; it returns no sentiment scores or key phrases. Azure AI Language provides both sentiment analysis and key phrase extraction. Content Safety would be correct when moderating user-generated content for abuse.
- ✗
Azure AI Language Understanding (LUIS)
Why it's wrong here
LUIS performs intent classification and entity extraction from utterances, not sentiment scoring or key-phrase extraction. It is tempting because it also processes natural-language text, but the required sentiment labels and key phrases come from Azure AI Language's sentiment analysis and key phrase extraction features, not LUIS's intent model.
- ✓
Azure AI Language - Sentiment Analysis and Key Phrase Extraction
Why this is correct
Sentiment analysis returns positive, negative, or neutral labels, while key phrase extraction pulls the salient terms such as 'great battery life'. Both capabilities sit within Azure AI Language, so one resource satisfies the stem's dual requirement without separate services.
Go deeper
Related to this question
About these practice questions
This AI-102 question is part of Courseiva's 761-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.