Docker Interview Questions 2026
Docker is essential for deploying AI applications in production. These questions cover containerization, Docker Compose, multi-stage builds, and production deployment patterns.
What is Docker and why is it essential for AI deployments?
Docker is a containerization platform that packages your application and all its dependencies into a container — a portable, isolated environment. For AI: (1) Reproducibility — same Python version, library versions everywhere, (2) Dependency isolation — no conflicts between projects, (3) Scalability — easily spin up multiple instances, (4) CI/CD integration — build once, deploy anywhere. Without Docker, "it works on my machine" kills AI deployments.
What is a multi-stage Docker build and why use it for Python AI apps?
Multi-stage builds use multiple FROM statements — early stages compile/build, the final stage only copies what's needed. For AI apps: Stage 1 installs all build dependencies (gcc, wheel compilers for numpy/scipy) → Stage 2 copies only the installed packages. Result: image size goes from 2GB to 500MB. Smaller images: faster pulls, less attack surface, lower cloud storage costs.
How do you handle secrets and API keys in Docker containers?
Never hardcode secrets in Dockerfiles or images. Methods in order of security: (1) Environment variables via --env-file (OK for development), (2) Docker secrets (for Swarm), (3) Kubernetes secrets with external secret management (AWS Secrets Manager, HashiCorp Vault) injected at runtime. For AI apps with multiple API keys (OpenAI, Pinecone, AWS), use a secrets manager that rotates keys automatically.
Explain Docker Compose for local AI development.
Docker Compose orchestrates multiple containers for local development. For an AI app stack: services: app (FastAPI), db (PostgreSQL), cache (Redis), vectorstore (Chroma). Define in docker-compose.yml with ports, volumes for hot reload, and environment variables. Use depends_on with health checks. In AI development, also add: a mock LLM service for testing without API costs, and Prometheus + Grafana for local monitoring.
What is the difference between CMD and ENTRYPOINT in a Dockerfile?
ENTRYPOINT defines the main executable that always runs. CMD provides default arguments that can be overridden. Best practice: ENTRYPOINT ["uvicorn"] CMD ["main:app", "--host", "0.0.0.0"]. Running docker run image main:app --reload overrides CMD but not ENTRYPOINT. For AI model serving containers, ENTRYPOINT is the server, CMD configures it — allows flexible runtime configuration.
How do you optimize Docker image build time for AI projects?
Layer caching is key. Order Dockerfile instructions from least to most frequently changing: (1) Base image, (2) System dependencies (apt install), (3) Python dependencies (COPY requirements.txt, RUN pip install), (4) Application code (COPY . .). This way, changing your code doesn't invalidate the cached Python dependencies layer. Use .dockerignore to exclude __pycache__, .env, and model weight files.
How do you run GPU workloads with Docker?
Use NVIDIA Container Toolkit: install nvidia-docker2, then use --gpus all flag or in docker-compose: deploy: resources: reservations: devices: - capabilities: [gpu]. Base image: use nvidia/cuda:12.0-runtime-ubuntu22.04 instead of plain python. For AI inference, PyTorch detects GPU automatically. Monitor GPU usage inside container with nvidia-smi. For production, use dedicated GPU instances (AWS p3, g4 families).
What is the difference between Docker volumes and bind mounts?
Volumes are managed by Docker at /var/lib/docker/volumes — portable, backed up, shareable between containers. Bind mounts link a host directory into the container — used for hot reload in development (COPY . . becomes volume: ./:/app). For AI: use volumes for model weights and database files in production, bind mounts for development to avoid rebuilding the image on every code change.
How do you implement health checks for a Docker AI service?
Add HEALTHCHECK in Dockerfile: HEALTHCHECK --interval=30s --timeout=10s --retries=3 CMD curl -f http://localhost:8000/health || exit 1. In FastAPI, create a /health endpoint that checks: DB connection, vector store availability, LLM API reachability. Docker marks container unhealthy if checks fail, and orchestrators (ECS, Kubernetes) restart unhealthy containers automatically.
How do you debug a Docker container that keeps crashing?
Debug steps: (1) docker logs <container_id> to see output, (2) docker run --rm -it image bash to get a shell, (3) Override ENTRYPOINT: docker run --entrypoint bash image, (4) Add --restart=no to prevent restart loop, (5) Check exit codes: 137 = OOM killed (increase memory limit), 1 = application error, 127 = command not found. For AI services crashing on startup: often missing env vars or failed model loading.
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