The answer is sentiment analysis with opinion mining, the correct Azure AI Language feature for extracting aspect-based opinions from text. This feature goes beyond basic sentiment polarity by detecting specific targets or aspects—such as battery life, camera quality, or screen brightness—and then assigning a sentiment score to each one, enabling the company to see exactly what customers praise or criticize. On the AI-900 exam, this scenario tests your understanding of how opinion mining extends standard sentiment analysis for granular insights, often appearing in questions about customer feedback or product reviews. A common trap is confusing this with key phrase extraction, which only pulls out terms without sentiment, or standard sentiment analysis, which gives an overall positive/negative score for the entire text. Remember the memory tip: “Opinion mining links the feeling to the thing”—if you need to know what people love or hate about specific features, choose opinion mining.
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
This AI-900 practice question tests your understanding of describe features of natural language processing workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
Option C is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 identifies important phrases but does not associate sentiment with specific aspects.
✗
Named entity recognition
Why it's wrong here
Named entity recognition extracts entities like product names or dates, not opinions or aspects.
✓
Sentiment analysis with opinion mining
Why this is correct
This feature detects sentiment at both the document and aspect level, making it ideal for understanding praise or criticism about specific features.
Related concept
Read the scenario before looking for a memorised answer.
✗
Language detection
Why it's wrong here
Language detection only identifies the language used in the text, providing no information about content or sentiment.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Trap categories for this question
Keyword trap
Key phrase extraction identifies important phrases but does not associate sentiment with specific aspects.
Detailed technical explanation
How to think about this question
Under the hood, Azure's opinion mining uses a deep learning model that performs aspect-based sentiment analysis (ABSA), which first identifies aspect terms (e.g., 'battery life') and then classifies the associated sentiment as positive, negative, or neutral. This feature is powered by a transformer-based architecture that can handle co-reference and negation, such as correctly interpreting 'The battery life is not great' as a negative opinion about battery life. In real-world scenarios, this enables companies to aggregate granular feedback across thousands of reviews to prioritize product improvements.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of Natural Language Processing workloads on Azure — This question tests Describe features of Natural Language Processing workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Sentiment analysis with opinion mining — Option C is correct because 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.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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