Question: In a concise analytical response, explain how the training and testing setup could make a document-classifier look better than it would perform after deployment.

Sample answer: The model was trained and evaluated using scanned document images collected from a controlled source, so the test score reflected performance on that same kind of input. In deployment, however, the app received photos taken with phones, which often included skewed pages, shadows, motion blur, glare, and heavy compression. Because the evaluation set did not match the real input distribution, strong test performance did not mean the system would work equally well for phone photos.

Key points:

• Training and test data came from scanned documents.
• Deployment inputs were phone photos of documents.
• Phone photos often had skew, blur, shadows, glare, or compression.
• Success on the scanned-document test set did not prove generalization to deployment inputs.

Rubric: A strong response identifies both data sources, describes the visual or capture-quality differences, and explains that the mismatch between evaluation data and deployment data makes the test result unreliable for predicting real-world performance. Partial credit should be given for noting the mismatch even if the generalization issue is not fully explained.