Title: Quiz 2 — Docker, GPU Runtime, and Dev-Only Tools (Ollama, LM Studio)
Course: AIINFRA 200: Production Inference Serving & GPU Orchestration > Section: Week 02 — Docker, GPU Runtime, and Dev-Only Tools (Ollama, LM Studio)

Q: What must you configure before docker run --gpus all ... nvidia-smi will succeed, even if the NVIDIA driver is already installed on the host?
- Nothing else is needed
- The NVIDIA Container Toolkit, with the Docker runtime configured via `nvidia-ctk runtime configure --runtime=docker`
- A full CUDA toolkit installation on the host
- A Kubernetes cluster
A: The NVIDIA Container Toolkit, with the Docker runtime configured via `nvidia-ctk runtime configure --runtime=docker`

Q: You must install the full CUDA toolkit on the host machine in order to run GPU-accelerated containers.
- True
- False
A: False

Q: Which environment variable controls which driver libraries (e.g., compute, utility) are mounted inside a GPU container?
- NVIDIA_VISIBLE_DEVICES
- NVIDIA_DRIVER_CAPABILITIES
- CUDA_HOME
- DOCKER_GPU_MODE
A: NVIDIA_DRIVER_CAPABILITIES

Q: In a Docker Compose file, which block is used to request GPU resources for a service?
Feedback: Sample/expected answer: The ****`deploy.resources.reservations.devices`**** block (with ****`driver: nvidia`**** and ****`capabilities: [gpu]`****)

Q: Ollama and LM Studio are designed to replace production serving engines like vLLM or TGI at scale.
- True
- False
A: False

Q: What port does Ollama's REST API default to?
- 8080
- 11434
- 5000
- 3000
A: 11434

Q: Why does placing a frequently-changed instruction (like COPY of source code) before a rarely-changed instruction (like installing dependencies) in a Dockerfile bloat rebuild times?
Feedback: Sample/expected answer: Because Docker invalidates the build cache for every layer after the first changed instruction, so a change to source code forces the dependency-install layer (and everything after it) to rebuild from scratch even though the dependencies didn't change.

Q: What is the recommended hybrid workflow for going from model evaluation to a servable API using this week's dev-only tools?
- Use LM Studio in production directly
- Use LM Studio's model browser to evaluate candidate models, then productionize the chosen model with Ollama or a real serving engine
- Use Ollama to browse models and LM Studio to serve production traffic
- Skip evaluation and deploy the first model you find directly to production
A: Use LM Studio's model browser to evaluate candidate models, then productionize the chosen model with Ollama or a real serving engine