Ray for Distributed AI Quiz

Answers and explanations appear as you take the quiz.

1. What problem does Ray primarily solve?
   • Scaling Python and AI workloads from a single machine to a large cluster with minimal code change
   • Labeling images automatically
   • Compressing model files
   • Designing neural network architectures
2. In Ray, what is the difference between a 'task' and an 'actor'?
   • Tasks are for images and actors are for text
   • A task is a stateless parallel function call; an actor is a stateful worker that holds state
   • They are identical
   • Tasks run only on CPUs and actors only on printers
3. What does Ray return immediately when you launch a remote task?
   • The final result, always
   • An error message
   • A future (object reference) that you later resolve with ray.get()
   • A copy of the entire cluster
4. Which Ray library is designed for distributed hyperparameter search?
   • Ray Serve
   • Ray Data
   • RLlib
   • Ray Tune
5. Which object type do Ray’s docs explicitly say can be read from same-node object-store memory without a copy?
   • A NumPy array stored in Ray’s object store
   • Any Python object whenever an ObjectRef is passed
   • An object requested from another node with no network transfer
   • Only actor handles stored in a separate database
6. Which Ray library would you use to deploy models behind a scalable, autoscaling endpoint?
   • Ray Tune
   • Ray Serve
   • Ray Train
   • Ray Data
7. How does Ray Serve decide when to add replicas in its built-in request-driven autoscaling?
   • It reads the model’s training loss
   • It changes the number of model parameters
   • It monitors ongoing requests or queue demand against configured targets
   • It scales only when a human edits the deployment
8. How does Ray move an object when a task on a different node needs it?
   • It shares the owner’s local memory directly across every node
   • It automatically copies all objects into each node’s GPU memory
   • It sends only an ObjectRef and never transfers the object data
   • It transfers the object over the cluster network; serialization, locality, and object size affect cost