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PMLE Practice Question: A logistics company uses a regression model to…
A logistics company uses a regression model to predict delivery times. The model currently uses features: distance (km), traffic index, weather condition, and time of day. The data scientist notices that the model's predictions are systematically too low for deliveries during peak traffic hours. Which action would best address this issue?
⚠ Common exam trap
Google Cloud often tests the misconception that systematic bias is always due to insufficient data or the wrong model type, when in fact it is frequently caused by missing feature interactions that can be fixed with simple feature engineering.
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
✓
Add a cross-feature that multiplies distance by traffic index
The model's systematic underestimation during peak traffic hours indicates a missing interaction effect between distance and traffic. Adding a cross-feature (distance × traffic index) allows a linear model to capture the non-linear relationship where traffic disproportionately increases delivery time over longer distances. This directly addresses the bias without discarding useful data or unnecessarily complicating the model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a deep neural network model
Why it's wrong here
Replacing the regression model with a deep neural network does not address the underlying issue of feature insufficiency. The systematic error stems from missing variables that capture non-linear traffic dynamics, whereas a neural network requires high-quality input data to minimise bias. This approach is tempting because deep learning excels at identifying intricate patterns in unstructured datasets like images or audio, but it cannot compensate for omitted predictors in structured tabular data.
- ✗
Remove the traffic index feature as it is causing bias
Why it's wrong here
Removing a key feature would worsen the model.
- ✓
Add a cross-feature that multiplies distance by traffic index
Why this is correct
This interaction term allows the model to capture the combined effect.
- ✗
Collect more training data during peak traffic hours
Why it's wrong here
More data helps but does not directly address the systematic bias due to missing interaction.
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