The correct answer is C because containers package the application and its dependencies so that workloads can run consistently across environments. NVIDIA’s NGC documentation states: “Containers encapsulate an application along with its libraries and other dependencies to provide reproducible and reliable execution of applications and services without the overhead of a full virtual machine.”

NVIDIA’s HPC SDK Container Guide also states that containers bundle “the entire application user space environment into a single image,” making the application environment “portable and consistent” and independent of the underlying host software configuration. It further states that container images can be deployed widely with confidence that results will be reproducible. Therefore, using containers to package dependencies is the best practice for consistency when scaling AI workloads across different environments.

Why the other options are incorrect: Boosting hardware speed does not guarantee software consistency. Documenting differences between test and production is useful, but it does not itself create reproducible runtime environments. Containers directly solve the dependency and environment consistency problem.