Case context: A team evaluates on mobile app images. It has 5,000 mobile app images and 40 times as many internet images, but it cannot afford a neural network large enough to model both sources equally well.

Question: Diagnose the team's capacity problem and decide how it should weight the internet images according to the source.

Sample answer: The much larger internet dataset may demand far more computation and representation capacity if the model must perform well on both sources. Because the team cannot build a sufficiently large network and its evaluation distribution is mobile app images, it should pay closer attention to matching that target distribution. It can still use the internet images, but should give them a much lower weight as a compromise.

Key points:

• Internet images greatly outnumber target-distribution images.
• Modeling both sources can exceed available computation or capacity.
• The mobile app distribution should receive greater attention because it matches evaluation.
• Internet images should remain in training but receive a much lower weight.

Rubric: The response should identify competition for limited capacity, prioritize the mobile app dev/test distribution, and recommend lower—not zero or higher—weight for the internet images.