AI0-001 AI Infrastructure and Technologies Practice Question
An MLOps engineer is deploying a scikit-learn random forest model to a Kubernetes cluster for a low-traffic internal API. The team wants to avoid maintaining a custom Flask wrapper and prefers a standard serving solution that supports REST and gRPC. Which serving component should they choose?
⚠ Common exam trap
The trap here is equating 'model serving on Kubernetes' with 'write a Flask container', ignoring that KServe provides ready-made runtimes for common frameworks like scikit-learn.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
KServe with a scikit-learn model server runtime.
KServe offers a purpose-built scikit-learn runtime that serves pickled models over REST and gRPC on Kubernetes, with autoscaling and rollout features included. It eliminates the need for a bespoke Flask wrapper, which the team explicitly wants to avoid. The other options either require custom code, target GPU deep learning workloads, or demand unsupported format conversion.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
KServe with a scikit-learn model server runtime.
Why this is correct
KServe provides standardized model serving on Kubernetes with built-in support for REST and gRPC, autoscaling, and canary rollouts. Its scikit-learn runtime loads the pickled model directly without a custom Flask wrapper. This matches the requirement for a standard, low-maintenance serving solution for a random forest model on Kubernetes.
- ✗
NVIDIA Triton Inference Server with a Python backend script that loads the pickle file.
Why it's wrong here
Triton is optimized for deep learning frameworks and GPU inference; using its Python backend to run a scikit-learn random forest adds unnecessary complexity and provides no GPU benefit for a CPU-bound tree model. It also still requires custom script maintenance, which the team wants to avoid. KServe's native scikit-learn runtime is the more appropriate fit.
- ✗
TensorFlow Serving configured with a SavedModel export of the random forest.
Why it's wrong here
TensorFlow Serving expects TensorFlow SavedModel or similar formats and is designed for TensorFlow graphs, not scikit-learn pickles. Converting a random forest to SavedModel is non-trivial and unsupported natively. This choice introduces format-conversion work without delivering the desired simplicity.
- ✗
A Kubernetes Deployment running a Flask app that loads the model and exposes only a REST endpoint.
Why it's wrong here
This is exactly the custom Flask wrapper the team wants to avoid. It lacks native gRPC support, autoscaling integration, and standardized health checks that KServe provides. While it works technically, it increases maintenance burden and does not meet the stated preference for a standard serving solution.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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