Q: Your team heavily integrates a massive 5GB Artificial Intelligence Machine Learning model binary dynamically into a Python application. The CI pipeline fetches this huge file heavily from S3 natively on every test execution, making pipelines take violently over 45 minutes simply downloading files. How do you resolve this?

You cannot fundamentally cache a massive 5GB file efficiently via standard ephemeral CI caching mechanisms natively (they often forcefully timeout or natively exceed size quotas). *Fix:* You must heavily transition to a Pre-baking Strategy. Instead of fetching the model heavily at build execution time natively, heavily create a dedicated cron pipeline that expressly securely cooks the 5GB model deep into a foundational Docker Base Image natively (company/ml-base-model:v2). The standard CI pipeline seamlessly securely updates its Dockerfile expressly pointing to FROM company/ml-base-model:v2. Because the immense model is already physically pre-cached securely in the immutable layer natively on the registry/nodes, the pipeline safely completes instantaneously natively.