Original practice scenario inspired by BHP Group Limited's public business context; this is not a reported interview question or a description of its internal systems. A fictional mining logistics tool recommends haul routes using road availability and vehicle constraints. Updates may be delayed. Design the information flow and user-facing behavior without assuming the software autonomously controls vehicles.
Read model answer +
Treat a recommendation as a decision based on a particular constraint snapshot. Dynamic road closures and vehicle restrictions require freshness checks and an operational override path, not just a shortest-path algorithm.
Work through it
Version road and vehicle constraints, recording source timestamps and validity. Reject or flag recommendations when critical constraints are missing or stale.
Compute feasible routes before optimizing distance or time. Include the snapshot version and assumptions with each recommendation so operators can understand and invalidate it.
Recompute affected recommendations after relevant changes and test delayed closure messages, conflicting restrictions, and loss of connectivity. Preserve a documented manual fallback.
Tradeoffs to discuss
Frequent recomputation improves responsiveness but increases cost and can create unstable recommendations. Debouncing must not suppress critical invalidations.
Common pitfalls
The shortest route may be infeasible for a vehicle.
An available route service does not imply its underlying constraint data is current or operationally approved.
Original practice scenario inspired by BHP Group Limited's public business context; this is not a reported interview question or a description of its internal systems. A synthetic exploration model predicts a material property from sampled locations. Nearby samples are highly correlated, and some sites are sampled much more densely. How would you evaluate generalization to an unsampled area?
Read model answer +
Spatial dependence makes random sample splits optimistic. Evaluate held-out areas that resemble the intended new locations, and keep the prediction claim separate from decisions requiring broader geological evidence.
Work through it
Define spatial blocks or site groups before splitting, with buffer distances where appropriate. Fit transformations only on the training portion.
Compare against simple spatial or site-level baselines. Report error by held-out area and sample density, not only a pooled metric dominated by dense sites.
Assess uncertainty calibration and whether the new area lies outside the training feature range. Describe an abstention or additional-sampling policy for unsupported predictions.
Tradeoffs to discuss
Larger blocks test stronger extrapolation but leave fewer independent evaluation units. A single spatial split can be unstable, so repeat defensible blocked evaluations.
Common pitfalls
Nearby train and test points can leak local structure.
Narrow confidence intervals over correlated rows overstate how much independent evidence the dataset contains.
