AI-102 Plan and manage an Azure AI solution Practice Question
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. 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 service custom text classification model. You need to ensure that the training data is balanced and representative. What should you do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Split the labeled data into training and test sets, ensuring each class has similar proportions.
Option C is correct because splitting labeled data into training and test sets while ensuring each class has similar proportions (stratified split) is a standard practice for balanced and representative training data. This approach prevents class imbalance from skewing model evaluation metrics and ensures the model generalizes well to unseen data. In Azure AI Language custom text classification, this is critical for achieving reliable performance across all classes.
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.
✗
Use only the most frequent labels to train the model.
Why it's wrong here
Ignoring minority classes results in a biased model.
✗
Oversample the minority classes to match the majority class size.
Why it's wrong here
Oversampling can lead to overfitting and does not guarantee representativeness.
✓
Split the labeled data into training and test sets, ensuring each class has similar proportions.
Why this is correct
A ensures balanced representation and proper evaluation.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use all labeled data for training and rely on cross-validation.
Why it's wrong here
Cross-validation without a held-out test set can overestimate performance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse data balancing techniques (like oversampling) with the fundamental requirement of a representative train-test split, leading them to choose Option B or D instead of recognizing that stratified splitting is the direct and correct method for ensuring balanced and representative data in Azure AI Language custom text classification.
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) fine-tuned on your labeled data. A stratified train-test split ensures that each class's distribution in the test set mirrors the training set, which is essential for accurate performance metrics like precision, recall, and F1-score. In real-world scenarios, such as classifying customer support tickets where 'billing' issues dominate, a stratified split prevents the model from being evaluated only on majority classes and missing poor performance on rare categories like 'security'.
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Split the labeled data into training and test sets, ensuring each class has similar proportions. — Option C is correct because splitting labeled data into training and test sets while ensuring each class has similar proportions (stratified split) is a standard practice for balanced and representative training data. This approach prevents class imbalance from skewing model evaluation metrics and ensures the model generalizes well to unseen data. In Azure AI Language custom text classification, this is critical for achieving reliable performance across all classes.
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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Question Discussion
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