Case context: A team has built a customer support intent classifier with a modest neural network and 8,000 labeled chat transcripts. Its performance has stalled. They can either greatly enlarge the network or gather 60,000 additional labeled transcripts, but they cannot do both right away.

Question: If the best result will eventually come from having both a much larger network and much more data, what should the team conclude about its long-term plan, even if it must act in stages?

Sample answer: The team should conclude that reaching the strongest possible performance will likely require both changes: a larger network and a much larger dataset. Improving only one of them may help for a while, but each has a limit on its own. The long-term plan should therefore include a path to scale both model capacity and training data.

Key points:

• Improving only one factor has limits.
• The long-term target is scaling both dimensions.
• Best performance requires both a much larger network and much more data.

Rubric: Evaluates whether the learner understands that the highest performance usually depends on increasing both model size and data volume over time.