AI-102 Practice Question: Implement natural language processing solutions
You are using the Azure AI Language service to process customer reviews. You need to extract the following insights: overall sentiment, key phrases, and entity types (such as product names). Which THREE operations should you call?
⚠ Common exam trap
It's easy for candidates to confuse Language Detection or PII Detection with the required insights, mistakenly thinking language identification or privacy data extraction fulfills the need for sentiment, key phrases, and entity types, when in fact they serve entirely different purposes.
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
Sentiment Analysis (C) is correct because it returns the overall sentiment of the review (positive, negative, neutral, or mixed) along with confidence scores, which is exactly the first insight required. Key Phrase Extraction (A) is correct because it identifies the main talking points in the review text, satisfying the key phrases requirement. Entity Recognition (E) is correct because it extracts and classifies named entities such as product names, locations, and organizations, which covers the entity types requirement. Language Detection (B) is not needed because the scenario does not ask for identifying the review's language, and PII Detection (D) is not needed because the scenario does not require detecting or redacting personal information.
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 returns the salient terms within review text, satisfying the requirement to surface key phrases. It is one of three separate Azure AI Language operations, alongside Sentiment Analysis and Named Entity Recognition, so it cannot alone deliver sentiment scores or entity types.
- ✗
Language Detection
Why it's wrong here
Language detection returns the detected language and confidence score for input text, not sentiment, key phrases or entities. It is the correct call when routing multilingual documents to the appropriate language-specific model before analysis, which this single-language review scenario does not require.
- ✓
Sentiment Analysis
Why this is correct
Sentiment Analysis returns an overall sentiment label and confidence scores for each review, directly satisfying the requirement to extract overall sentiment. It is one of the three distinct Azure AI Language operations needed, alongside key phrase extraction and entity recognition.
- ✗
PII Detection
Why it's wrong here
PII detection identifies and redacts personal data such as names, addresses and phone numbers; it returns entity categories for privacy, not sentiment scores, key phrases or product entities. It would be the right operation when the requirement is to mask personal information before storage or sharing.
- ✓
Entity Recognition
Why this is correct
Entity Recognition identifies and classifies named entities such as product names within the review text, satisfying the requirement to extract entity types. It operates independently of sentiment and key phrase extraction, making it the third required Azure AI Language operation.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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