The answer is sentiment analysis with opinion mining, the correct Azure AI Language feature for this task. This capability goes beyond basic sentiment scoring by performing aspect-based sentiment analysis, which identifies specific opinion targets—like product names or features—and the sentiment expressed toward each one. In the example, it would correctly extract “X100” as the product, then link “positive” to “battery life” and “negative” to “screen,” matching the requirement to extract both the named entities and their associated sentiments from customer emails. On the AI-900 exam, this question tests your understanding of Azure AI Language’s advanced features; a common trap is confusing standard sentiment analysis (which gives an overall positive/negative score for the whole sentence) with opinion mining (which breaks sentiment down by target). Remember the memory tip: “Opinion mining = sentiment + target,” so if the task asks for both what is being talked about and how it’s felt, opinion mining is your answer.
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 customer service department wants to automatically extract the names of products mentioned in customer emails and the sentiment expressed about each product. For example, from the sentence 'The battery life of the X100 is excellent, but the screen is too dark,' they need to identify 'X100' and associate 'positive' sentiment with 'battery life' and 'negative' sentiment with 'screen'. Which Azure AI Language feature should they use?
Why wrong: Incorrect. Custom text classification assigns categories to the whole document or sentence, not specific entities with their own sentiment.
B
Key phrase extraction
Why wrong: Incorrect. Key phrase extraction returns important phrases but does not provide sentiment or associate specific opinions with entities.
C
Entity linking
Why wrong: Incorrect. Entity linking identifies entities and connects them to a knowledge base, but does not extract sentiment about those entities.
D
Sentiment analysis with opinion mining
Correct. Opinion mining (a component of sentiment analysis) extracts aspects (such as product names or features) and the expressed sentiment toward each aspect.
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 D is correct because sentiment analysis with opinion mining is specifically designed to extract both the sentiment (positive, negative, neutral) and the associated target (e.g., 'battery life', 'screen') from text. This feature goes beyond simple sentiment scoring by identifying the opinion target and the sentiment expressed toward it, which directly matches the requirement to extract product names and their associated sentiments from customer emails.
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.
✗
Custom text classification
Why it's wrong here
Incorrect. Custom text classification assigns categories to the whole document or sentence, not specific entities with their own sentiment.
✗
Key phrase extraction
Why it's wrong here
Incorrect. Key phrase extraction returns important phrases but does not provide sentiment or associate specific opinions with entities.
✗
Entity linking
Why it's wrong here
Incorrect. Entity linking identifies entities and connects them to a knowledge base, but does not extract sentiment about those entities.
✓
Sentiment analysis with opinion mining
Why this is correct
Correct. Opinion mining (a component of sentiment analysis) extracts aspects (such as product names or features) and the expressed sentiment toward each aspect.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse key phrase extraction (Option B) with sentiment analysis with opinion mining, because key phrases can include product names, but key phrase extraction does not provide any sentiment association, which is the core requirement of the question.
Trap categories for this question
Keyword trap
Incorrect. Key phrase extraction returns important phrases but does not provide sentiment or associate specific opinions with entities.
Detailed technical explanation
How to think about this question
Under the hood, sentiment analysis with opinion mining uses a deep learning model that performs aspect-based sentiment analysis (ABSA). It first identifies opinion targets (e.g., 'battery life', 'screen') and then classifies the sentiment for each target as positive, negative, or neutral. In the example, the model would output two opinion-sentiment pairs: ('battery life', 'positive') and ('screen', 'negative'), even though the overall sentence sentiment might be mixed. This feature is critical in scenarios like analyzing product reviews or customer feedback where granular insights are needed.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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 D is correct because sentiment analysis with opinion mining is specifically designed to extract both the sentiment (positive, negative, neutral) and the associated target (e.g., 'battery life', 'screen') from text. This feature goes beyond simple sentiment scoring by identifying the opinion target and the sentiment expressed toward it, which directly matches the requirement to extract product names and their associated sentiments from customer emails.
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.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A retail company wants to automatically determine whether customer reviews are positive, negative, or neutral. Which prebuilt Azure AI Language feature should they use?
easy
A.Key phrase extraction
B.Language detection
✓ C.Sentiment analysis
D.Entity recognition
Why C: Sentiment analysis is the correct Azure AI Language feature because it is specifically designed to classify text into positive, negative, or neutral sentiment categories. This prebuilt capability analyzes customer reviews at the document and sentence level, returning a sentiment label and confidence scores, which directly meets the requirement of automatically determining review polarity.
Variation 2. A customer service team wants to automatically determine whether each customer feedback message is positive, negative, or neutral. Which Azure AI Language feature should they use?
easy
A.Key phrase extraction
B.Language detection
✓ C.Sentiment analysis
D.Entity recognition
Why C: Sentiment analysis is the correct Azure AI Language feature because it is specifically designed to classify text into positive, negative, or neutral sentiments. This directly matches the customer service team's requirement to automatically determine the sentiment of each feedback message. Other features like key phrase extraction or entity recognition do not perform sentiment classification.
Last reviewed: Jun 11, 2026
Question Discussion
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