These are the correct answers because the project steering committee plays a role in approving deviations from the chosen methodology, when required to address project issues, and setting the criteria for selecting agile or waterfall methodology to be used on internal projects. The steering committee does not design a methodology, but rather chooses one from the existing ones. The steering committee does not enforce that corporate project stage gates are part of the chosen methodology but rather ensures that the chosen methodology aligns with the corporate project stage gates.

C is the correct answer, as creating a custom metadata type and including this in your deployment to production is the best way to manage this deployment. A custom metadata type is a metadata component that can store important configuration data, such as custom settings, and can be deployed along with other components, such as Apex classes. A is incorrect, as scripting the deployment of all functionality via the Force.com Migration Tool is not a good way to manage this deployment, as it does not address the issue of deploying the custom settings that hold the configuration data. B is incorrect, as manually deploying and populating custom settings in production using a change set is not a good way to manage this deployment, as it can introduce errors and inconsistencies, as well as require additional steps and permissions. D is incorrect, as manually deploying and populating the custom settings in production prior to the Apex class deployment is not a good way to manage this deployment, as it can create dependency and timing issues, as well as require additional steps and permissions. You can learn more about this topic in the [Custom Metadata Types] module on Trailhead.

Testing load considering 50% more call volume is a reasonable test load consideration, as it can simulate the expected future growth and ensure that the application can handle the increased demand without compromising the performance or functionality.

D is the correct answer, as automated testing is the best approach to mitigate the risk of bugs being inadvertently introduced to production. Automated testing can help to ensure that the code meets the quality and functionality standards, as well as detect and prevent any errors or regressions before deploying to production. A is incorrect, as user acceptance testing is not enough to mitigate the risk of bugs, as it depends on the manual testing and feedback of the end users, which can be subjective and incomplete. B is incorrect, as the use of an agile methodology is not directly related to mitigating the risk of bugs, as it is a way of managing the project scope, requirements, and delivery. C is incorrect, as requirements traceability is not directly related to mitigating the risk of bugs, as it is a way of tracking the requirements and their fulfillment.
You can learn more about this topic in the Testing Strategies module on Trailhead.

Two testing types that an architect should recommend to ensure users don't face platform slowdowns during peak business hours are: stress testing and performance testing. Stress testing is the process of testing the system under extreme load conditions, such as a large number of concurrent requests, to determine its breaking point and identify any potential bottlenecks or failures. Performance testing is the process of testing the system under normal or expected load conditions, such as the average number of requests per day, to measure its response time, throughput, and resource utilization. These testing types can help ensure that the web service can handle the expected volume and frequency of requests, and that it meets the performance and reliability requirements. Unit testing is not a testing type that an architect should recommend for this scenario, as it is the process of testing individual units of code, such as methods or classes, to verify their functionality and logic. Load testing is also not a testing type that an architect should recommend for this scenario, as it is the process of testing the system under a specific load condition, such as a fixed number of requests, to evaluate its behavior and performance. See Testing RESTful Web Services for more details.