Question: Explain why machine learning work usually depends on repeated experimentation, even for skilled practitioners. How should this affect project planning?

Sample answer: In machine learning, it is rarely possible to know ahead of time which model, feature set, or training setup will work best for a new problem. Because of that uncertainty, progress usually comes from trying one idea after another, measuring results, and adjusting the approach. Even highly experienced teams often test many alternatives before reaching a strong solution. As a result, a project should be planned around fast prototyping, short feedback cycles, and room for revision instead of assuming the first design will be the final one.

Key points:

• It is hard to predict in advance which approach will perform best.
• Skilled practitioners still need to try many alternatives.
• Planning should support quick experiments and repeated revision.

Rubric: The response should state that the best ML approach is difficult to predict in advance, mention that experts still try many ideas, and conclude that project plans should allow rapid iteration and experimentation.