BentoML and Model Packaging Quiz

Answers and explanations appear as you take the quiz.

1. What is the standardized, deployable unit that BentoML produces?
   • A Bento
   • A Pod
   • A Wheel
   • A Chart
2. What does BentoML's adaptive micro-batching primarily improve?
   • Model accuracy
   • Throughput on accelerators like GPUs
   • Source code readability
   • Training speed
3. In BentoML 1.1’s Runner-based architecture, what handles compute-heavy model execution separately from API logic?
   • The Bento manifest
   • The OCI registry
   • Runners
   • The HTTP request schema
4. Which command turns a built Bento into an OCI Docker image?
   • bentoml deploy
   • bentoml serve
   • bentoml train
   • bentoml containerize
5. Which of these is a key reason BentoML embeds dependency versions in a Bento?
   • To make builds reproducible across machines
   • To reduce model size
   • To improve GPU clock speed
   • To encrypt the model
6. Which decorator marks a class-based object as a BentoML Service in the current API?
   • @bentoml.runner
   • @bentoml.service
   • @bentoml.container
   • @bentoml.model_api
7. Which command packages a BentoML service definition and its environment into a Bento?
   • bentoml launch
   • bentoml register
   • bentoml build
   • bentoml compile
8. What does a method decorated as a BentoML API provide?
   • A GPU driver
   • A Kubernetes node pool
   • A model-training dataset
   • An inference endpoint exposed by the Service