What is an MVM?

• A. A simple prototype model used to test whether AI can solve the problem.
• B. A complex model for production deployment.
• C. A dataset used for validation.
• D. A metric for model evaluation.

Answer: (A)

An MVM is a small, quick-to-build prototype model used to test whether the AI approach can solve the problem. The idea is to validate feasibility early, learn what works, and uncover major obstacles before committing to a full-scale solution. It should be simple and fast to run, so you can iterate based on real results rather than assumptions.

This is why the option describing a simple prototype model best fits: it captures the purpose of testing whether the AI approach can actually solve the problem, without jumping to production-ready complexity, using a dataset, or relying on a separate evaluation metric. For example, you might train a tiny model on a subset of data to see if the approach yields any useful signal; if it does, you know you’re on the right track and can plan the next steps. If it doesn’t, you can pivot early rather than investing heavily in a full-scale solution.