- A
Undersample the majority class before training
Why wrong: Undersampling reduces data size and may lose valuable information, which is less optimal than using class weights.
- B
Use the CREATE MODEL statement with CLASS_WEIGHTS = {'0': 0.2, '1': 0.8}
BigQuery ML supports class weights to handle imbalance by assigning higher weights to the minority class.
- C
Use SMOTE via TRANSFORM clause in BigQuery ML
Why wrong: SMOTE is not a built-in feature in BigQuery ML; class weights are the supported method.
- D
Oversample the minority class by duplicating rows
Why wrong: Duplicating rows can lead to overfitting and does not add new information; class weights are preferred.
Question 413 of 1,000
PDE Preparing and Using Data for Analysis Practice Question
This PDE practice question tests your understanding of preparing and using data for analysis. 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.
A data engineer needs to create a BigQuery ML model for predicting customer churn using a dataset with 10 million rows and 50 features. The dataset is highly imbalanced (5% churn). Which approach should the engineer use to handle class imbalance during model training?
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
✓
Use the CREATE MODEL statement with CLASS_WEIGHTS = {'0': 0.2, '1': 0.8}
BigQuery ML supports class weights for imbalanced datasets via the CLASS_WEIGHTS option in CREATE MODEL. This assigns higher weight to the minority class without generating synthetic data. SMOTE is not available in BigQuery ML. Undersampling the majority class would lose data, and oversampling with duplication could introduce bias.
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.
- ✗
Undersample the majority class before training
Why it's wrong here
Undersampling reduces data size and may lose valuable information, which is less optimal than using class weights.
- ✓
Use the CREATE MODEL statement with CLASS_WEIGHTS = {'0': 0.2, '1': 0.8}
Why this is correct
BigQuery ML supports class weights to handle imbalance by assigning higher weights to the minority class.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use SMOTE via TRANSFORM clause in BigQuery ML
Why it's wrong here
SMOTE is not a built-in feature in BigQuery ML; class weights are the supported method.
- ✗
Oversample the minority class by duplicating rows
Why it's wrong here
Duplicating rows can lead to overfitting and does not add new information; class weights are preferred.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Preparing and Using Data for Analysis — study guide chapter
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FAQ
Questions learners often ask
What does this PDE question test?
Preparing and Using Data for Analysis — This question tests Preparing and Using Data for Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the CREATE MODEL statement with CLASS_WEIGHTS = {'0': 0.2, '1': 0.8} — BigQuery ML supports class weights for imbalanced datasets via the CLASS_WEIGHTS option in CREATE MODEL. This assigns higher weight to the minority class without generating synthetic data. SMOTE is not available in BigQuery ML. Undersampling the majority class would lose data, and oversampling with duplication could introduce bias.
What should I do if I get this PDE question wrong?
Identify which PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
This PDE 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 PDE exam.
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