Capital One interview questions

Ask what decision the output drives. Approve or decline is binary, so use precision and recall at the operating threshold, set by error costs. How much, like a loss amount, is regression, judged by MAE or RMSE.

Often it's both: predict the probability of default and the loss if it happens, and combine them into expected value per decision. Agree the metric with stakeholders before modelling, and validate on a time-based split.

XGBoost learns a default direction for missing values at every split: it tries sending them left and right and keeps whichever lowers the loss, so missingness itself can carry signal.

Detect overfitting by tracking training against validation loss over boosting rounds, on a time-based or grouped split. Control it with early stopping, shallower trees, larger min child weight, a lower learning rate, row and column subsampling, and L1 or L2 on leaf weights.

Score every authorisation inside the 50 ms budget: precompute velocity features, like recent spend per card, in a streaming feature store, and serve a gradient-boosted model in-process.

Labels lag 14 days, so train only on transactions old enough to be labelled, and monitor with faster signals like customer disputes. With 0.2% fraud, keep probabilities calibrated and set the threshold from the cost matrix: decline when the expected fraud loss exceeds the expected cost of blocking a good customer. Add rules, a review queue for the grey zone, and shadow-test new models.

Ship model, feature code and threshold as one versioned artifact in a registry. Run it in shadow mode, then canary it on a slice of traffic, with one-step rollback to the previous version.

Monitor latency, score distribution and feature drift, and precision and recall once labels arrive. For low latency: precomputed features, a compact model served in-process or on an optimised runtime, and a rules fallback if the model times out.