Question: Explain why changing a model’s design can sometimes be a better strategy than simply making the model larger or collecting more examples.

Sample answer: Changing the structure of a model can produce a large improvement because it may correct both underfitting and overfitting at the same time. That makes it a potentially strong way to fit the task more closely. The downside is that this kind of change is usually difficult to choose well and harder to implement than basic scaling steps. It is also less certain: adding training data or increasing capacity tends to be more straightforward and easier to predict, while design changes can lead to very different outcomes.

Key points:

• May improve both bias and variance together
• Usually harder to select and apply correctly
• Outcomes are less predictable than scaling data or model size

Rubric: A complete response should mention the possible gain of improving both bias and variance, and also note the practical difficulty and uncertainty of architecture changes compared with simpler scaling methods.