Title: Quiz 9 — Ray Serve and KServe for Scalable Model Serving
Course: AIINFRA 200: Production Inference Serving & GPU Orchestration > Section: Week 09 — Ray Serve and KServe for Scalable Model Serving

Q: Which statement best describes Ray Serve?
- A Kubernetes-only CRD for serving models
- A scalable, framework-agnostic model serving library that can serve models or arbitrary Python business logic
- A GPU driver management tool
- A container registry for ML models
A: A scalable, framework-agnostic model serving library that can serve models or arbitrary Python business logic

Q: Ray Serve can only be deployed on a Kubernetes cluster.
- True
- False
A: False

Q: What signal does the Ray Serve autoscaler primarily monitor to decide replica counts?
- GPU temperature
- Queue depth/queue sizes
- Raw request counts only, with instant reaction
- Disk I/O
A: Queue depth/queue sizes

Q: Name two of the advanced distributed serving patterns supported by Ray Serve LLM's new APIs with vLLM.
Feedback: Sample/expected answer: Any two of: prefill-decode disaggregation, data-parallel attention, expert parallelism.

Q: What is the primary custom resource definition (CRD) that KServe provides on Kubernetes?
- RayService
- InferenceService
- ModelDeployment
- ServingPod
A: InferenceService

Q: ModelMesh is a required core component of every KServe installation.
- True
- False
A: False

Q: A team needs an all-in-one platform spanning scalable training, tuning, AND serving for complex custom ML pipelines across multi-cloud and hybrid environments. Which of the two systems covered this week is the better fit, and why?
Feedback: Sample/expected answer: Ray Serve — because it is a general-purpose library that runs anywhere (not just Kubernetes) and excels at composing complex custom pipelines in Python, unlike KServe which is built specifically around Kubernetes primitives.

Q: Roughly how much throughput improvement did the recent HAProxy-based ingress upgrade deliver for Ray Serve LLM compared to the prior proxy?
- Up to 2x higher throughput
- Up to 5x higher throughput and 8x lower latency
- No measurable change
- Up to 50x higher throughput
A: Up to 5x higher throughput and 8x lower latency