AI-102 Practice Question: Implement natural language processing solutions
A retailer wants to analyze thousands of product reviews per day and needs to know which aspects of the products customers mention, such as battery life or screen quality, and whether sentiment toward each aspect is positive or negative. The reviews are already stored as text in an Azure SQL Database. Which Azure AI Language feature should you use?
⚠ Common exam trap
The trap here is stopping at document-level sentiment, which gives one overall polarity and never attributes that polarity to individual product aspects.
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
✓
Opinion mining with sentiment analysis on the analyze-text endpoint
Opinion mining, exposed as an extension of sentiment analysis on the analyze-text endpoint, returns aspect-level targets with individual sentiment labels and confidence scores. That output maps directly to the retailer's need to know which product aspects are mentioned and whether sentiment toward each is positive or negative, without the labeling overhead of a custom classification project.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Custom text classification with a project trained on labeled reviews
Why it's wrong here
Custom text classification assigns whole-document labels such as complaint or praise, and it requires labeled training data plus a deployed project. It does not return aspect-level targets and their individual sentiments, so it would not identify battery life or screen quality as aspects with separate polarities.
- ✗
Document-level sentiment analysis with the default opinionMining setting disabled
Why it's wrong here
Document-level sentiment returns a single positive, negative, or neutral label for the whole review plus sentence-level scores, but it does not attribute sentiment to specific product aspects. Disabling opinion mining removes the aspect-level detail the retailer needs, so the output would not reveal which features customers praise or criticize.
- ✓
Opinion mining with sentiment analysis on the analyze-text endpoint
Why this is correct
Opinion mining extends sentiment analysis by returning aspect-level assessments, so the response identifies terms such as battery life or screen quality and pairs each with a target and a sentiment. This directly answers the requirement to know which product aspects customers mention and whether sentiment toward each is positive or negative.
- ✗
Key phrase extraction on each review to list the most frequent terms
Why it's wrong here
Key phrase extraction surfaces salient terms but assigns no polarity. Knowing that battery life appears frequently does not reveal whether customers are happy or unhappy with it, so this feature cannot satisfy the requirement to determine sentiment toward each mentioned aspect of the product.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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