- A
Switch to a prebuilt text classification model
Why wrong: Prebuilt models may not match custom categories.
- B
Increase the number of training epochs
Why wrong: More epochs can cause overfitting, not generalization.
- C
Reduce the confidence threshold for classification
Why wrong: Threshold adjustment does not improve model accuracy.
- D
Add more labeled data from actual production tickets
Diverse data helps the model learn patterns present in production.
Question 225 of 993
Improve Custom Text Classification — More Labeled Data | Azure AI Engineer Associate Explained
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.
A company uses Azure AI Language Service for custom text classification. The model is trained to classify support tickets into categories. After deployment, the model performs well on the test set but poorly on new incoming tickets. Which action should be taken to improve generalization?
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
✓
Add more labeled data from actual production tickets
Option D is correct because adding more labeled data from actual production tickets helps the model learn the true distribution of real-world inputs, reducing overfitting to the test set. The model's poor performance on new tickets indicates it memorized patterns specific to the training data rather than generalizing. Incorporating production data directly addresses the distribution shift between the test set and live traffic.
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.
- ✗
Switch to a prebuilt text classification model
Why it's wrong here
Prebuilt models may not match custom categories.
- ✗
Increase the number of training epochs
Why it's wrong here
More epochs can cause overfitting, not generalization.
- ✗
Reduce the confidence threshold for classification
Why it's wrong here
Threshold adjustment does not improve model accuracy.
- ✓
Add more labeled data from actual production tickets
Why this is correct
Diverse data helps the model learn patterns present in production.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often mistakenly tune hyperparameters (epochs, confidence threshold) or switch to a prebuilt model, but the real issue is distribution shift between test data and production data. Adding representative labeled data from production is the correct solution.
Detailed technical explanation
How to think about this question
Under the hood, custom text classification in Azure AI Language uses a transformer-based neural network that learns decision boundaries from labeled examples. Overfitting occurs when the model captures noise or spurious correlations in the training set, which is common with small or unrepresentative datasets. Adding production data introduces variability that forces the model to learn more robust features, effectively performing a form of empirical risk minimization on the true data distribution.
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.
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: Add more labeled data from actual production tickets — Option D is correct because adding more labeled data from actual production tickets helps the model learn the true distribution of real-world inputs, reducing overfitting to the test set. The model's poor performance on new tickets indicates it memorized patterns specific to the training data rather than generalizing. Incorporating production data directly addresses the distribution shift between the test set and live traffic.
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.
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Same concept, more angles
1 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's custom text classification to categorize support tickets. The model was trained with 5000 labeled examples and achieves 90% accuracy. However, for a specific category (e.g., 'billing'), the model frequently misclassifies tickets that contain both billing and technical issues. Which action should you take to improve classification for this category?
medium- A.Reduce the number of categories to simplify the classification.
- ✓ B.Add more labeled examples for the 'billing' category, especially those that are mixed with other categories.
- C.Increase the number of training epochs to further train the model.
- D.Use a different classification algorithm, such as a neural network.
Why B: Option B is correct because adding more labeled examples for the 'billing' category, especially those that are mixed with other categories, will help the model learn to distinguish them better. Option A is wrong because reducing the number of categories may not address the specific confusion. Option C is wrong because increasing the training epochs may lead to overfitting. Option D is wrong because using a different algorithm is not an option in Azure AI Language's custom text classification.
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Last reviewed: Jul 4, 2026
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