Title: Quiz 8 — Deploying an AI Inference App to Kubernetes
Course: AIINFRA 101: Applied Infrastructure & Containerization > Section: Week 08 — Deploying an AI Inference App to Kubernetes

Q: In a minimal AI inference app on Kubernetes, what is the primary role of the Service object?
- Running the containerized model-serving code
- Providing a stable network endpoint that load-balances traffic across Pods
- Storing the trained model weights
- Scheduling Pods onto specific GPU nodes
A: Providing a stable network endpoint that load-balances traffic across Pods

Q: High nvidia-smi GPU utilization always indicates that an inference workload is running efficiently.
- True
- False
A: False

Q: What is the KServe InferenceService best described as?
- A raw container image for running any Python script
- A Kubernetes CRD that orchestrates model serving, delegating actual inference to a backend runtime
- A replacement for the Kubernetes scheduler
- A GPU driver required for Triton to run
A: A Kubernetes CRD that orchestrates model serving, delegating actual inference to a backend runtime

Q: Name one thing vLLM is specifically optimized for.
Feedback: Sample/expected answer: High-throughput LLM serving (via PagedAttention memory management)

Q: Which inference server is best known for serving models from multiple frameworks (PyTorch, ONNX, TensorFlow) side by side?
- vLLM
- Triton Inference Server
- KServe
- kubectl
A: Triton Inference Server

Q: By default, the Kubernetes scheduler is aware of GPU interconnect topology and will always place multi-GPU workloads on physically nearby GPUs.
- True
- False
A: False

Q: Before a Deployment in a local kind cluster can use an image you built with docker build, what step must you perform first?
- Push the image to Docker Hub
- Run `kind load docker-image <image>:<tag>` to load it into the cluster
- Nothing — kind automatically has access to your local Docker daemon's images
- Rebuild the image using `minikube build`
A: Run `kind load docker-image <image>:<tag>` to load it into the cluster

Q: Name one cloud-managed toolchain mentioned in this week's lecture that simplifies deploying open-source LLMs on Kubernetes.
Feedback: Sample/expected answer: Azure KAITO (or Google GKE's LLM deployment operator)