PDE Preparing and Using Data for Analysis Practice Question
You are building a binary classification model using AutoML Tables on Vertex AI. The dataset has a severe class imbalance (1% positive class). Which strategy should you use to handle the imbalance?
⚠ Common exam trap
Confusing scikit-learn/XGBoost-style class_weight parameters with Vertex AI AutoML Tables, which instead uses a dataset weight column to handle class imbalance.
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 a weight column to assign higher weights to the minority class.
The correct option is D: use a weight column in AutoML Tables to assign higher weights to the minority class. In Vertex AI AutoML Tables, you can add a weight column to the training data and specify it as the weight column, so minority-class rows contribute more to the loss. This directly counteracts the 1% positive-class imbalance without changing the data distribution. Options A and C are not supported preprocessing steps in AutoML Tables, since it manages data splitting and training internally and does not expose SMOTE or manual downsampling. Option B is incorrect because AutoML Tables does not automatically correct severe class imbalance; you must supply weights or adjust the optimization objective. Option D is therefore the appropriate, supported mechanism for this scenario.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Oversample the minority class using SMOTE before training.
Why it's wrong here
SMOTE is not directly integrated with AutoML Tables; you would need to preprocess separately, but AutoML can handle imbalance internally.
- ✗
Do nothing; AutoML Tables automatically handles class imbalance.
Why it's wrong here
AutoML Tables does apply some techniques but setting class_weight explicitly gives better control.
- ✗
Downsample the majority class to match the minority class size.
Why it's wrong here
Downsampling can discard valuable data and may reduce model performance; AutoML handles imbalance better.
- ✓
Use a weight column to assign higher weights to the minority class.
Why this is correct
AutoML Tables allows setting class weights to address imbalance; it will adjust the loss function accordingly.
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