Case context: A team is building a classifier that identifies product placements on store shelves. Their labeled training images mostly come from a public photo archive because those images are easy to download in large quantities. They randomly split those archive images 80%/20% into training and test sets.

Question: What problem should the team identify with this split, and why?

Sample answer: The split is likely misleading because both the training set and the test set come from the same archive-photo source, while the model will actually be used on photos taken by store employees on handheld devices. The archive images and the deployment images may differ in angle, lighting, clutter, and framing, so a strong score on the random split would not reliably predict real performance. The issue is a source mismatch between the evaluation data and the intended use environment.

Key points:

• Training and test images both come from the archive source
• Real use will involve handheld device photos from stores
• Random splitting does not address the source mismatch
• Test results may overstate real-world accuracy

Rubric: Full credit identifies the mismatch between archive images and handheld store photos, and explains that the split is misleading because it does not reflect the deployment distribution.