An online grocery retailer is reviewing a list of candidate projects and wants to commit only to those where machine learning would add genuine value over hand-written rules or basic reporting. Which TWO of the following tasks are well suited to a machine learning approach? Select TWO.
- ACalculating each order's delivery fee from a published table that maps postcode bands to fixed charges set by management.
- BSuggesting the next products an individual shopper is most likely to buy, drawn from years of varied browsing and purchase history. Correct
- CRounding every basket total to two decimal places before the payment gateway processes the charge to the customer.
- DSending an automatic email when a delivery is marked complete, using a fixed template the marketing team approved.
- EForecasting demand for perishable stock next week from several years of sales, seasonality, and weather signals. Correct
Why A is wrong: A fixed lookup table gives one exact charge per band, so a deterministic rule is correct and cheaper; ML would add risk without benefit here.
Why B is correct: Personalisation learns each shopper's patterns from rich behavioural history, a relationship too complex and shifting for hand-written rules to capture.
Why C is wrong: Rounding is a precise arithmetic operation with one defined answer, so a simple formula is exact and ML offers nothing useful.
Why D is wrong: A status change triggering a fixed template is straightforward event-driven logic; there is no pattern to learn, so ML is unnecessary.
Why E is correct: Demand forecasting models nonlinear patterns across many noisy historical signals, which is exactly where ML outperforms fixed thresholds or averages.