## Appendix A Full Questionnaire (Updated Version--60 Questions)
### Infrastructure Debt--Questions 31 to 34  
Question 31: Are there mechanisms in place for automated monitoring and alerting of infrastructure performance metrics (e.g., Central Processing Unit (CPU) usage, memory utilization, network throughput)?  
Stakeholder: Organizers  
Score: 4  
Justification:  
Maintainability: Automated monitoring can reduce technical debt by making it easier to maintain the system.  
Future-proofing: Early detection of performance issues can prevent the accumulation of technical debt related to system degradation.  
Example: Implementing comprehensive monitoring from the start can avoid the need for costly refactoring of the monitoring system later on. Our competition's infrastructure includes an automated monitoring system that continuously tracks CPU and GPU usage, memory utilization, and network throughput. If any metric crosses a predefined threshold, such as CPU usage exceeding 90%, the system triggers an alert to our technical team, prompting immediate investigation and intervention to prevent any performance degradation during the competition.  
Question 32: Has provision been made in the infrastructure for sufficient computing resources available?  
Stakeholder: Organizers and Participants  
Score: 3  
Justification:  
Scalability: While important, over-provisioning resources can lead to unnecessary complexity and costs, contributing to technical debt.  
Cost Management: Balancing resources with actual needs can minimize expenses and reduce the risk of investing in technologies that may become obsolete.  
Example: Investing in scalable cloud services can prevent over-commitment to a particular infrastructure setup, reducing long-term technical debt.  
Question 33: Have you developed a robust data pipeline for easy experimentation with AI algorithms?  
Stakeholder: OrganizersJustification: Providing the necessary infrastructure to support efficient experimentation is essential in AI competition platforms. A well-designed data pipeline enables participants to iterate quickly, test diverse algorithms, and fine-tune their models--ultimately improving submission quality and raising the overall level of competition. Sound infrastructure management plays a critical role in the platform's success.  
Example: For instance, in an image recognition challenge, a participant aiming to compare multiple machine learning models can benefit from a reliable data pipeline. It allows them to preprocess large datasets with ease, train and evaluate various architectures and hyperparameter settings, and streamline the experimentation process for faster optimization.  
37. Question 37: Does the model allow debugging by observing the step-by-step computation of training or inference on a single example? Stakeholder: Participants Score: 3 Justification: The ability to debug a model at a granular level is important for understanding and improving its decision-making process. However, it might be less critical than the other two questions if the model is performing well overall. Example: If an AI agent makes an unexpected move in a game, being able to step through the computation process can help identify why that decision was made, whether it was due to a flaw in the model or an unforeseen aspect of the game environment.