- A
Provide more labeled training examples that cover a wider variety of writing styles and sentiments.
More diverse training data helps the model generalize better and capture nuances.
- B
Pre-process the text with key phrase extraction to highlight important terms before sentiment analysis.
Why wrong: Key phrase extraction does not improve sentiment analysis; the model already uses the full text.
- C
Use the opinion mining feature to capture sentiment targets and associated opinions.
Opinion mining provides aspect-based sentiment, which can improve overall sentiment detection by analyzing specific targets.
- D
Use the basic sentiment analysis API without any customization.
Why wrong: The basic API is what you already have; using it alone does not improve accuracy.
- E
Enable the domain-specific model for 'Reviews' if available.
Domain-specific models are pre-trained on relevant data and can improve accuracy for that domain.
How to Improve Sentiment Analysis Accuracy for Domain-Specific Text
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.
Your organization uses Azure AI Language to perform sentiment analysis and opinion mining on product reviews. You notice that the sentiment scores are often neutral even when the review text contains clearly positive or negative opinions. You suspect the model is not capturing the nuances. Which three actions could improve the sentiment analysis accuracy? (Choose three.)
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
Provide more labeled training examples that cover a wider variety of writing styles and sentiments.
Option A is correct because providing more labeled training examples that cover a wider variety of writing styles and sentiments directly improves the custom model's ability to learn nuanced patterns. Azure AI Language's custom sentiment analysis relies on supervised learning; more diverse, high-quality labeled data helps the model generalize better and reduces the tendency to default to neutral scores for ambiguous or complex reviews.
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.
- ✓
Provide more labeled training examples that cover a wider variety of writing styles and sentiments.
Why this is correct
More diverse training data helps the model generalize better and capture nuances.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Pre-process the text with key phrase extraction to highlight important terms before sentiment analysis.
Why it's wrong here
Key phrase extraction does not improve sentiment analysis; the model already uses the full text.
- ✓
Use the opinion mining feature to capture sentiment targets and associated opinions.
Why this is correct
Opinion mining provides aspect-based sentiment, which can improve overall sentiment detection by analyzing specific targets.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use the basic sentiment analysis API without any customization.
Why it's wrong here
The basic API is what you already have; using it alone does not improve accuracy.
- ✓
Enable the domain-specific model for 'Reviews' if available.
Why this is correct
Domain-specific models are pre-trained on relevant data and can improve accuracy for that domain.
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 may assume pre-processing with key phrase extraction (option B) or using the basic API (option D) can fix model accuracy issues, when in fact only custom training (Azure AI Language custom sentiment analysis), opinion mining, or domain-specific models address the root cause of neutral scores due to lack of nuance.
Trap categories for this question
Keyword trap
Key phrase extraction does not improve sentiment analysis; the model already uses the full text.
Detailed technical explanation
How to think about this question
Azure AI Language's sentiment analysis uses a deep neural network trained on a large corpus, but the pre-built model may assign neutral scores when sentiment is expressed with subtle language, sarcasm, or mixed opinions. Custom sentiment analysis with labeled examples allows fine-tuning of the model's weights, while the opinion mining feature (option C) extracts sentiment targets and associated opinions at a finer granularity, enabling detection of sentiment even when the overall document score is neutral. The domain-specific model for 'Reviews' (option E) is pre-trained on review-specific data, which improves accuracy for common review patterns like 'good product but poor packaging'.
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.
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: Provide more labeled training examples that cover a wider variety of writing styles and sentiments. — Option A is correct because providing more labeled training examples that cover a wider variety of writing styles and sentiments directly improves the custom model's ability to learn nuanced patterns. Azure AI Language's custom sentiment analysis relies on supervised learning; more diverse, high-quality labeled data helps the model generalize better and reduces the tendency to default to neutral scores for ambiguous or complex reviews.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on AI-102
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 company uses Azure AI Language for sentiment analysis on customer feedback. They notice that the sentiment scores for mixed reviews are often neutral when they should be slightly positive. They need to improve the accuracy for these mixed reviews without labeling new data. Which approach should you recommend?
hard- A.Adjust the sentiment threshold to require a higher confidence for neutral.
- ✓ B.Enable opinion mining to capture aspects and opinions separately.
- C.Use a prebuilt sentiment analysis model with a different language.
- D.Create and train a custom sentiment analysis model using labeled feedback data.
Why B: Option B is correct because enabling opinion mining allows the model to extract aspect-level sentiments, which helps to correctly interpret mixed reviews where overall sentiment is neutral but specific aspects are positive. This improves accuracy without requiring labeled data. Option A (adjusting threshold) is a heuristic that may reduce classification quality. Option C (using a different language model) is irrelevant to the problem. Option D (custom model) requires labeled data, contradicting the constraint.
Variation 2. A company uses Azure AI Language Service to analyze customer feedback. They notice that the sentiment scores for negative reviews are often incorrectly labeled as neutral. Which configuration should be adjusted to improve accuracy?
medium- A.Deploy the Language service in a different Azure region
- B.Increase the confidence threshold for sentiment classification
- ✓ C.Create a custom sentiment analysis model using Custom Text Classification
- D.Enable Key Phrase Extraction to preprocess the text
Why C: The Azure AI Language Service's pre-built sentiment analysis model may not capture domain-specific nuances in customer feedback, leading to misclassification of negative reviews as neutral. By creating a custom sentiment analysis model using Custom Text Classification (option C), you can train the model on your labeled data to improve accuracy for your specific use case.
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Last reviewed: Jul 4, 2026
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