Containerization Fundamentals Questions

Foundational knowledge of container technology, focused on Docker and container workflows. Topics include what containers are and how they differ from virtual machines, container images and registries, building and reading Dockerfiles, running containers, volume and file system mounting, basic container networking, image layering and size optimization, and common use cases such as reproducible deployments for machine learning and microservices. Candidates should be able to explain the container lifecycle, why containerization matters in DevOps, and demonstrate simple hands on tasks like writing a basic Dockerfile and running containers locally.

HardTechnical

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List and explain at least five container security hardening practices relevant to ML inference containers (e.g., least privilege, scanning images, reducing attack surface). For each, give an example configuration or command that enforces it.

MediumTechnical

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You're using docker-compose to run a model server and a Postgres DB for integration tests. Containers cannot connect to Postgres; list a methodical debugging checklist using docker-compose and networking commands to identify the issue.

MediumTechnical

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You must deploy an inference container where the model file is updated weekly. Discuss strategies to update the model without rebuilding container images every time, while ensuring each deployed instance references the correct model version and can be rolled back if needed.

HardTechnical

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Describe how you would detect and remediate dependency vulnerabilities introduced by base image updates while minimizing disruption to running ML services. Include an automated scanning cadence, emergency patching workflow, and communication plan.

MediumTechnical

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Explain the trade-offs between building Docker images in CI using Docker-in-Docker (dind), Kaniko, and BuildKit (buildx). For a cloud CI environment with limited privileges, which would you choose and why? Discuss caching and security implications.

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