Minimum Viable Product

Correct Answer: Develop the MVP with the AI meal suggestion feature first, then use iterative feedback to evaluate the need for a customization feature.

In the context of Scrum and creating an MVP (Minimum Viable Product), the goal is to quickly deliver a product with the minimum set of features that early adopters will use and provide feedback on. Starting with the core feature based on the product vision (in this case, the AI meal suggestion) aligns with this goal. Iteratively developing the product based on real user feedback allows for informed decisions about which features to add next, such as the customization option. It ensures that resources are wisely invested in features that users actually want and will use. The wrong answers, although plausible, suggest approaches that either dilute the focus on fast learning through early user feedback, involve significant upfront investment without validated learning, or unnecessarily delay the development and learning process.

Correct Answer: Start with core budgeting and investing educational features in the MVP and incrementally add the risk assessment feature based on feedback.

The correct answer avoids overcomplication and aligns with agile principles of starting with a minimal set of features to satisfy early adopters and then expanding based on real user feedback. Extensive market testing or predicting future demand for the risk assessment feature, though important, can delay the MVP unnecessarily and are not aligned with the agile principle of quick iterations based on feedback. Integrating a third-party tool right from the start might solve the immediate need but could lead to issues with cost, complexity, and dependency, which isn't ideal for an MVP focused on core functionality.

Correct Answer: Identifying the core feature that provides the maximum value to the user

When developing an MVP (Minimum Viable Product), the focus should be on identifying and implementing the core feature that provides the maximum value to the user with minimal effort, to test hypotheses about product-market fit fast and efficiently. Extensive market research, while valuable, might delay the MVP. Advanced analytics and comprehensive user manuals are less crucial at the MVP stage and can be elaborated upon based on feedback and data gathered post-launch.