AIF-C01 Fundamentals of AI and ML Practice Question
A retail company uses a machine learning model to forecast daily product demand. The model is a time series model that uses historical sales data. The model has been performing well, but recently the forecasts have been consistently too low, leading to stockouts. The data scientist notices that the model was trained on data up to last year, and the company has since launched a successful marketing campaign that increased sales by 20%. The data scientist needs to update the model to reflect the new sales patterns. Which approach should the data scientist take?
⚠ Common exam trap
A common mix-up: candidates think a simple multiplicative adjustment (Option C) is sufficient, but the AWS exam tests the understanding that time series models must be retrained on the new distribution to maintain forecast accuracy, as static adjustments ignore changes in the underlying data-generating process.
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
✓
Retrain the model on the most recent data that includes the sales from the marketing campaign.
Retraining the model on the most recent data that includes the sales from the marketing campaign allows the model to learn the new underlying pattern in the time series. Since the model is a time series model, it relies on historical patterns to make forecasts; retraining on data that captures the 20% sales lift ensures the model adapts to the new demand level, reducing the persistent underforecasting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a feature for the marketing campaign and continue using the old model.
Why it's wrong here
Adding a campaign feature to a model still trained only on pre-campaign data leaves the learned weights unchanged, so the 20% uplift is never captured. Feature engineering suits models retrained on data containing that feature; here the stale training window itself must be refreshed.
- ✗
Switch to a different model type, such as ARIMA, without retraining.
Why it's wrong here
Swapping to ARIMA without retraining leaves the model fitted to pre-campaign sales, so forecasts stay systematically low regardless of algorithm. Changing model family suits cases where the existing algorithm cannot represent the data's structure; here the training data, not the algorithm, is stale.
- ✗
Multiply the model's predictions by 1.2 to account for the marketing campaign.
Why it's wrong here
A flat 1.2 multiplier assumes the campaign lifted every product, day and season identically, ignoring weekday and seasonal variation the time series encodes. Scaling predictions suits a known uniform uplift; here retraining on post-campaign data lets the model learn the actual pattern.
- ✓
Retrain the model on the most recent data that includes the sales from the marketing campaign.
Why this is correct
The marketing campaign shifted the underlying sales distribution, so the stale training data no longer represents current demand. Retraining on recent data that includes campaign-period sales lets the model learn the new pattern, correcting the systematic under-forecasting.
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