- A
Manually split the data into a stratified train/test set to ensure the same proportion of defaults in each.
Why wrong: Manually splitting the data is unnecessary because AutoML Tables automatically handles data splitting. The issue is class imbalance during training, not the split.
- B
Train multiple models with different algorithms (e.g., XGBoost, Random Forest) and blend them using a custom script.
Why wrong: Blending models with a custom script requires writing code, which is not allowed due to time constraints. AutoML Tables already uses ensemble methods internally.
- C
Enable 'Enable weighted evaluation' and set the optimization objective to 'Maximize recall at a specific recall@P%' with a target precision of 0.5.
This is correct because it uses AutoML Tables' built-in weighted evaluation and custom optimization objective to focus on recall for the minority class, without needing custom code.
- D
Under-sample the majority class to create a balanced dataset and retrain.
Why wrong: Under-sampling may discard valuable data and is not recommended. AutoML Tables provides built-in mechanisms like weighted evaluation to handle imbalance effectively.
PMLE Practice Question: A financial services company uses Vertex AI…
This PMLE practice question tests your understanding of pmle exam topics. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 financial services company uses Vertex AI AutoML Tables to build a credit risk model. The dataset contains 500,000 rows and 50 features, including loan amount, credit score, debt-to-income ratio, and employment length. The target variable is binary: 'default' (1) or 'no default' (0). The data is highly imbalanced, with only 2% defaults. The data scientist trains a model with AutoML Tables using default settings. The evaluation metrics show an AUC of 0.85, but the confusion matrix reveals that the model predicts 'no default' for almost all cases, missing most defaults. The data scientist needs to improve the model's ability to identify defaults without significantly increasing false positives. They have limited time and cannot write custom code. What should they do?
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
Enable 'Enable weighted evaluation' and set the optimization objective to 'Maximize recall at a specific recall@P%' with a target precision of 0.5.
Option C is correct because AutoML Tables allows you to set a custom optimization objective to handle class imbalance without custom code. By enabling weighted evaluation and setting the objective to 'Maximize recall at a specific recall@P%' with a target precision of 0.5, the model will be tuned to prioritize identifying defaults (recall) while maintaining a specified precision level, directly addressing the need to catch more defaults without a massive increase in false positives.
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.
- ✗
Manually split the data into a stratified train/test set to ensure the same proportion of defaults in each.
Why it's wrong here
Manually splitting the data is unnecessary because AutoML Tables automatically handles data splitting. The issue is class imbalance during training, not the split.
- ✗
Train multiple models with different algorithms (e.g., XGBoost, Random Forest) and blend them using a custom script.
Why it's wrong here
Blending models with a custom script requires writing code, which is not allowed due to time constraints. AutoML Tables already uses ensemble methods internally.
- ✓
Enable 'Enable weighted evaluation' and set the optimization objective to 'Maximize recall at a specific recall@P%' with a target precision of 0.5.
Why this is correct
This is correct because it uses AutoML Tables' built-in weighted evaluation and custom optimization objective to focus on recall for the minority class, without needing custom code.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Under-sample the majority class to create a balanced dataset and retrain.
Why it's wrong here
Under-sampling may discard valuable data and is not recommended. AutoML Tables provides built-in mechanisms like weighted evaluation to handle imbalance effectively.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that manual data splitting or resampling is necessary for imbalanced data in AutoML, when in fact AutoML Tables provides built-in optimization objectives and weighted evaluation to handle imbalance without data manipulation.
Detailed technical explanation
How to think about this question
AutoML Tables uses a weighted loss function during training when 'Enable weighted evaluation' is turned on, which assigns higher penalties to misclassifying the minority class. The 'Maximize recall at a specific recall@P%' objective directly optimizes the precision-recall trade-off, allowing the model to focus on a specific operating point on the ROC curve, which is more informative than AUC for imbalanced datasets. In practice, this is critical for credit risk models where the cost of missing a default (false negative) is much higher than a false positive.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
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FAQ
Questions learners often ask
What does this PMLE question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Enable 'Enable weighted evaluation' and set the optimization objective to 'Maximize recall at a specific recall@P%' with a target precision of 0.5. — Option C is correct because AutoML Tables allows you to set a custom optimization objective to handle class imbalance without custom code. By enabling weighted evaluation and setting the objective to 'Maximize recall at a specific recall@P%' with a target precision of 0.5, the model will be tuned to prioritize identifying defaults (recall) while maintaining a specified precision level, directly addressing the need to catch more defaults without a massive increase in false positives.
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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Last reviewed: Jun 30, 2026
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.
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