Describe features of Natural Language Processing workloads on Azure →hardMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
A consumer electronics company collects online reviews about their latest smartphone. They want to identify specific aspects that customers praise or criticize, such as battery life, camera quality, and screen brightness. Which Azure AI Language feature should they use to extract these aspect-based opinions?
⚠ Common exam trap
Microsoft often tests the distinction between general sentiment analysis and opinion mining, where candidates mistakenly choose standard sentiment analysis (not listed) or key phrase extraction, thinking it can extract aspects without the sentiment linkage.
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
✓
Sentiment analysis with opinion mining
Sentiment analysis with opinion mining is specifically designed to extract aspect-based opinions from text. In this scenario, the company needs to identify which aspects (e.g., battery life, camera quality) are praised or criticized, which requires both aspect detection and sentiment polarity assignment. Opinion mining extends standard sentiment analysis by linking sentiments to specific targets or aspects within the text.
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 it's wrong here
Key phrase extraction returns the most salient words or phrases in a text, such as 'battery life' or 'customer service,' but it performs no sentiment classification. The extracted phrases are presented without any polarity score and without any relationship to an expressed opinion, so you cannot determine whether the reviewer praised or criticized an aspect. It is a document-level vocabulary feature, not an aspect-level evaluation tool.
- ✗
Named entity recognition
Why it's wrong here
Named entity recognition (NER) labels mentions of predefined categories such as product names, dates, locations, or organizations—for example, identifying 'Acme Phone' or 'January 2024' in a review. It does not extract subjective attributes like 'battery life' or 'usability' as opinion targets, nor does it evaluate the writer's attitude toward those entities. NER is about resolving what is being referred to, not about how the referent is judged, so it cannot reveal whether a feature is praised or criticized.
- ✓
Sentiment analysis with opinion mining
Why this is correct
Sentiment analysis with opinion mining is the correct choice because it detects the overall sentiment polarity—positive, negative, or neutral—and then goes further by extracting the specific aspects of the product (such as 'camera quality' or 'battery durability') that are being commented on. For each aspect, it links opinion words to the target aspect and assigns a separate sentiment score, so you can see that customers praise the display but criticize the battery. This aspect-level output directly answers the company's goal of understanding praise or criticism about specific features.
- ✗
Language detection
Why it's wrong here
Language detection classifies the dominant language in each review—e.g., English, Spanish, or Japanese—by analyzing character patterns and token statistics. It is often used as a preliminary step to route text to the appropriate downstream models, but it carries no information about the subject matter or the reviewer's evaluation. A review being in English tells you nothing about which product feature is liked or disliked, so this service is irrelevant to the stated analysis goal.
Go deeper
Related to this question
Learn chapter
Azure Machine Learning Studio
Key term
Sentiment analysis
Sentiment analysis is a natural language processing technique that uses machine learning to determine the emotional tone or opinion expressed in a piece of text.
Key term
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
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