Question: In one to three sentences, explain when a random 80%/20% train/test split is a bad way to evaluate a system.

Sample answer: A random split is a poor choice when the data the model is trained on does not match the data it will face after deployment. For example, a model trained on studio product photos may need to work on customer photos taken in very different lighting.

Key points:

• The problem is a mismatch between training and deployment data.
• Random splitting can hide that mismatch.
• The training and target distributions should be similar for the split to be reliable.

Rubric: Full credit requires stating that the split fails when the training data distribution differs from the real-world use distribution; partial credit for mentioning a random split without naming the mismatch condition.