Which explanation is NOT one of the standard reasons a model can score well on the dev set but poorly on the test set when the two sets come from different distributions?

If the development set and the test set come from different _____, a performance gap is hard to interpret.

Match each distribution-shift situation to the most likely explanation.

Order the three explanations for why a model can look strong on development data but weak on test data when the data distributions are different.

When the test data are drawn from a tougher distribution than the development data, what is the most reasonable conclusion?

True or False: If a model scores lower on the test set than on the dev set, then the test set must be inherently more difficult.

A model may do well on the _____ set and still perform poorly on the test set if the two sets come from different distributions.

Match each dev/test diagnosis to the most appropriate next step.

Arrange the steps a practitioner follows when a spam filter succeeds on validation data but fails after deployment.

Why a good development score can still leave test results unexplained

Why a Model Looks Strong in Validation but Weak on Deployment Data

Three Reasons a Held-Out Test Set Can Look Worse

Why a Mismatched Validation Set Can Waste Effort