The correct answer is to use class weights in the CREATE MODEL statement, as this directly addresses the imbalance by penalizing misclassifications of the minority class more heavily during training. This works because BigQuery ML’s default optimization assumes balanced classes, so without class weights, the model becomes biased toward the majority class, inflating accuracy while missing rare positive cases. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding that AUC-ROC, not accuracy, is the reliable metric for imbalanced datasets—a common trap where candidates fixate on high accuracy scores. A key memory tip is to remember that class weights shift the decision boundary, while AUC-ROC evaluates ranking ability across all thresholds, making them a powerful pair for handling imbalanced dataset BigQuery ML workflows.
PMLE Solving business challenges with ML Practice Question
This PMLE practice question tests your understanding of solving business challenges with ml. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Refer to the exhibit. A data scientist is evaluating a binary classification model trained with BigQuery ML on an imbalanced dataset. The exhibit shows the output of ML.EVALUATE run on two different thresholds. Which TWO actions should the data scientist take to improve model performance? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use AUC-ROC as the evaluation metric instead of accuracy.
Option B is correct because AUC-ROC is insensitive to class imbalance and evaluates the model's ability to rank positive instances higher than negative ones across all thresholds, unlike accuracy which can be misleading when the majority class dominates. In BigQuery ML, ML.EVALUATE returns metrics like accuracy, precision, recall, and AUC-ROC; for imbalanced datasets, AUC-ROC provides a more reliable measure of discriminative power.
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.
✗
Add more features from the source data.
Why it's wrong here
Adding features may help overall performance but is not specifically targeted at handling imbalance.
✓
Use AUC-ROC as the evaluation metric instead of accuracy.
Why this is correct
AUC-ROC is robust to class imbalance and provides a better measure of model discrimination.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply SMOTE oversampling in the preprocessing pipeline.
Why it's wrong here
While oversampling can help, it is not a built-in feature of BigQuery ML and would require extra preprocessing steps.
✓
Use class weights in the CREATE MODEL statement.
Why this is correct
Class weights address class imbalance by penalizing misclassifications of the minority class more heavily.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of training iterations.
Why it's wrong here
Increasing iterations may lead to overfitting and does not directly address imbalance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that adding more data or features is a universal fix for imbalanced datasets, when in fact the core issue requires adjustments to the evaluation metric or the loss function (e.g., class weights) rather than simply increasing data volume or iterations.
Detailed technical explanation
How to think about this question
AUC-ROC measures the area under the receiver operating characteristic curve, which plots the true positive rate against the false positive rate at various threshold settings. In BigQuery ML, the CREATE MODEL statement supports class weights via the `class_weights` option, which assigns higher penalties to misclassifications of the minority class, effectively rebalancing the loss function during training. This is a built-in mechanism that avoids the need for external resampling and works directly with the model's optimization algorithm.
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.
Solving business challenges with ML — This question tests Solving business challenges with ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use AUC-ROC as the evaluation metric instead of accuracy. — Option B is correct because AUC-ROC is insensitive to class imbalance and evaluates the model's ability to rank positive instances higher than negative ones across all thresholds, unlike accuracy which can be misleading when the majority class dominates. In BigQuery ML, ML.EVALUATE returns metrics like accuracy, precision, recall, and AUC-ROC; for imbalanced datasets, AUC-ROC provides a more reliable measure of discriminative power.
What should I do if I get this PMLE 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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