- A
Create a custom NER project in Azure AI Language to extract product features, then use the opinion mining results and post-process to associate sentiments with the extracted features.
Custom NER can accurately extract the product features, and you can then use opinion mining scores to assign sentiment to each feature.
- B
Use the PII recognition feature to identify product features as entities.
Why wrong: PII recognition is for personally identifiable information, not product features.
- C
Use Conversational Language Understanding (CLU) to define intents for each product feature and train a model with labeled utterances.
Why wrong: CLU is for conversational flows, not for aspect-based sentiment analysis.
- D
Use the standard sentiment analysis API without opinion mining, and then use key phrase extraction to identify features and assign overall sentiment.
Why wrong: This approach loses aspect-level sentiment granularity.
AI-102 Practice Question: Implement natural language processing solutions
This AI-102 practice question tests your understanding of implement natural language processing solutions. 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.
You are a developer at a large retail company. The company receives thousands of product reviews daily. You need to build a solution that automatically categorizes reviews into positive, negative, and neutral sentiments, and also extracts key product features mentioned (e.g., battery life, screen quality) along with their associated sentiments. The solution must be scalable and cost-effective. You have access to Azure AI Language. You decide to use the built-in sentiment analysis and opinion mining features. However, after initial testing, you find that the opinion mining feature does not always correctly associate sentiments with the correct product features. For example, in the review 'The battery life is great but the screen is terrible', opinion mining might incorrectly associate 'terrible' with 'battery life'. You need to improve the accuracy of feature-sentiment association. What should you do?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"always"Why it matters: Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.
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
Create a custom NER project in Azure AI Language to extract product features, then use the opinion mining results and post-process to associate sentiments with the extracted features.
Option A is correct because it combines Azure AI Language's built-in opinion mining with a custom NER model to extract product features, then uses post-processing logic to correctly associate sentiments with those features. This approach addresses the core limitation of opinion mining, which can misalign sentiments when multiple features with contrasting sentiments appear in the same sentence. By first extracting features via custom NER, you can then map each sentiment phrase to the nearest or most relevant extracted entity, improving accuracy without sacrificing scalability or cost-effectiveness.
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.
- ✓
Create a custom NER project in Azure AI Language to extract product features, then use the opinion mining results and post-process to associate sentiments with the extracted features.
Why this is correct
Custom NER can accurately extract the product features, and you can then use opinion mining scores to assign sentiment to each feature.
Clue confirmation
The clue word "always" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use the PII recognition feature to identify product features as entities.
Why it's wrong here
PII recognition is for personally identifiable information, not product features.
- ✗
Use Conversational Language Understanding (CLU) to define intents for each product feature and train a model with labeled utterances.
Why it's wrong here
CLU is for conversational flows, not for aspect-based sentiment analysis.
- ✗
Use the standard sentiment analysis API without opinion mining, and then use key phrase extraction to identify features and assign overall sentiment.
Why it's wrong here
This approach loses aspect-level sentiment granularity.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume Azure AI Language's built-in opinion mining is fully reliable for all scenarios, but the exam tests the understanding that custom NER combined with post-processing is needed when the default model fails on complex multi-feature sentences.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Language's opinion mining uses a dependency parsing model to link aspects (targets) to opinions (sentiment expressions). However, when a sentence contains multiple aspects with conflicting sentiments, the parser can incorrectly assign an opinion to the wrong aspect due to proximity or syntactic ambiguity. A custom NER model trained on domain-specific product features (e.g., using a labeled dataset of reviews) can reliably identify all feature mentions, and a post-processing step (e.g., using the shortest dependency path or a co-reference resolution algorithm) can then re-associate each sentiment phrase to the correct feature, effectively correcting the parser's errors.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement natural language processing solutions — This question tests Implement natural language processing solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a custom NER project in Azure AI Language to extract product features, then use the opinion mining results and post-process to associate sentiments with the extracted features. — Option A is correct because it combines Azure AI Language's built-in opinion mining with a custom NER model to extract product features, then uses post-processing logic to correctly associate sentiments with those features. This approach addresses the core limitation of opinion mining, which can misalign sentiments when multiple features with contrasting sentiments appear in the same sentence. By first extracting features via custom NER, you can then map each sentiment phrase to the nearest or most relevant extracted entity, improving accuracy without sacrificing scalability or cost-effectiveness.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "always". Absolute qualifier. An answer using 'always' is only correct if there are genuinely no exceptions — absolute statements are often wrong in networking.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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