- A
Evaluate the model on a held-out test set that was not used during training.
A held-out test set gives an unbiased estimate of real-world performance.
- B
Review the confusion matrix to understand which classes are frequently misclassified.
The confusion matrix helps identify specific weaknesses in the model.
- C
Ensure the model achieves at least 95% accuracy on a cross-validation split.
Why wrong: Accuracy alone can be misleading, especially with imbalanced classes; precision and recall are more important.
- D
Use the training set to compute accuracy and ensure it is above 90%.
Why wrong: Performance on the training set is inflated and does not reflect generalization.
- E
Compare the model's performance to a baseline model that always predicts the most common class.
Why wrong: While comparing to a baseline is useful, it is not a required action before promotion; the question asks for actions to ensure performance requirements.
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 deploying an Azure AI Language custom text classification model. You need to ensure the model meets performance requirements before promoting it to production. Which two actions should you take? (Choose two.)
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
Evaluate the model on a held-out test set that was not used during training.
Option A is correct because evaluating the model on a held-out test set that was not used during training provides an unbiased estimate of its generalization performance. In Azure AI Language custom text classification, the training portal automatically splits your data into training and testing sets, but you can also upload your own test set. This ensures the model's accuracy reflects how it will perform on unseen production data, avoiding overfitting.
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.
- ✓
Evaluate the model on a held-out test set that was not used during training.
Why this is correct
A held-out test set gives an unbiased estimate of real-world performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Review the confusion matrix to understand which classes are frequently misclassified.
Why this is correct
The confusion matrix helps identify specific weaknesses in the model.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Ensure the model achieves at least 95% accuracy on a cross-validation split.
Why it's wrong here
Accuracy alone can be misleading, especially with imbalanced classes; precision and recall are more important.
- ✗
Use the training set to compute accuracy and ensure it is above 90%.
Why it's wrong here
Performance on the training set is inflated and does not reflect generalization.
- ✗
Compare the model's performance to a baseline model that always predicts the most common class.
Why it's wrong here
While comparing to a baseline is useful, it is not a required action before promotion; the question asks for actions to ensure performance requirements.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume a fixed accuracy threshold (like 95%) is required for production promotion, but Microsoft Azure AI Language custom text classification does not mandate any specific metric value—the focus is on evaluating generalization via a held-out test set and analyzing misclassifications with the confusion matrix.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Language custom text classification uses a transformer-based model (e.g., BERT variants) fine-tuned on your labeled data. The confusion matrix provides per-class precision, recall, and F1-score, which are critical for identifying class-specific weaknesses—especially in multi-class or multi-label scenarios where overall accuracy can be misleading. A real-world scenario is a legal document classifier where misclassifying a 'confidential' document as 'public' has severe consequences; the confusion matrix would reveal such class-level errors that overall accuracy might hide.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: Evaluate the model on a held-out test set that was not used during training. — Option A is correct because evaluating the model on a held-out test set that was not used during training provides an unbiased estimate of its generalization performance. In Azure AI Language custom text classification, the training portal automatically splits your data into training and testing sets, but you can also upload your own test set. This ensures the model's accuracy reflects how it will perform on unseen production data, avoiding overfitting.
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
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Last reviewed: Jul 4, 2026
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