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PMLE Practice Question: A manufacturing company wants to predict…
A manufacturing company wants to predict equipment failure using sensor data stored in BigQuery. They have limited ML expertise and want to use AutoML Tables. The data includes timestamps, numerical sensor readings, and a boolean 'failure' column. The dataset is highly imbalanced with only 1% failure cases. Which of the following is the most effective approach to handle the imbalance in AutoML Tables?
⚠ Common exam trap
A common mix-up: candidates assume manual resampling (downsampling or oversampling) is always required for imbalanced datasets, but AutoML Tables abstracts this complexity, and the exam tests whether you trust its built-in capabilities for low-code solutions.
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
✓
Let AutoML Tables handle the imbalance automatically; it has built-in techniques for class imbalance.
AutoML Tables has built-in techniques to handle class imbalance, such as automatically adjusting class weights and using stratified sampling during training. This allows the model to learn from the minority class without requiring manual data preprocessing, making it the most effective and simplest approach for users with limited ML expertise.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Let AutoML Tables handle the imbalance automatically; it has built-in techniques for class imbalance.
Why this is correct
AutoML Tables automatically adjusts for imbalance.
- ✗
Downsample the majority class to balance the dataset.
Why it's wrong here
Loses data and may reduce model accuracy.
- ✗
Use a custom loss function in the training configuration.
Why it's wrong here
AutoML Tables does not support custom loss functions.
- ✗
Oversample the minority class using SQL before training.
Why it's wrong here
AutoML Tables expects raw data; manual resampling may interfere with its optimizations.
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