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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 applies class weighting and other built-in techniques to address imbalanced datasets, so no manual resampling is needed. This suits the company's limited ML expertise while handling the 1% failure rate effectively during training.
- ✗
Downsample the majority class to balance the dataset.
Why it's wrong here
Downsampling discards 99% of the majority records, destroying the rare failure signal AutoML Tables needs and worsening recall. It is tempting because balancing class counts is a familiar technique, and would be reasonable when the majority class is noisy or the dataset is large enough that removal leaves ample training examples.
- ✗
Use a custom loss function in the training configuration.
Why it's wrong here
AutoML Tables exposes no custom loss function; training configuration is limited to predefined objectives, so this cannot be implemented. It is tempting because custom losses let experienced practitioners weight errors directly, and would be the right approach when training a custom TensorFlow or Vertex AI model where the loss is code-controlled.
- ✗
Oversample the minority class using SQL before training.
Why it's wrong here
Oversampling minority rows in SQL duplicates the 1% failure cases, encouraging overfitting to those exact records rather than improving generalisation. It is tempting because it preserves all majority data, and would suit smaller datasets where duplication adds diversity rather than repeating identical sensor readings.
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