Case context: A fintech company is building a large neural network to detect fraudulent card transactions. The team believes that if the model is still underperforming, the safest fix is to keep increasing model size and collecting more labeled transactions without stopping.

Question: What practical limits should the team expect if it tries to keep scaling this approach, and how should those limits change its planning?

Sample answer: In theory, a larger model trained on more data can continue to improve results. In practice, the team will run into two major limits. First, training ever-larger networks takes much longer and requires more compute, storage, and engineering effort. Second, there is only a finite amount of new labeled fraud data available, and each additional example can be costly to obtain. Because of these limits, the team should not assume that indefinite scaling is a realistic plan; it should budget for computation and accept that data growth will eventually slow or stop.

Key points:

• Notes that bigger models and more data can help in theory
• Identifies increasing compute time and cost as a constraint
• Identifies limited availability of additional labeled data
• Explains that these constraints make indefinite scaling impractical

Rubric: Full credit requires identifying both the compute/time limit and the finite-data limit, and connecting them to the feasibility of the team's scaling plan.