Module Quiz

Please answer the questions below to complete the test.

Question 1

Which location is most appropriate for defining an agent's authorization scope and output format requirements?

Select one answer

The user turn, so the operator can adjust them per request

The system prompt, so they apply to every interaction

A separate configuration file read by the agent at runtime

A comment at the top of the YAML manifest the agent produces

Question 2

What is the primary reason to pass kubectl output into a prompt before asking the LLM to modify a Kubernetes resource?

Select one answer

To reduce the number of tokens used in the prompt

To ensure the model reasons from actual current cluster state rather than stale training data

To satisfy a Meshery API requirement before running mesheryctl

To give the model access to the cluster credentials

Question 3

Which of the following techniques for getting reliable YAML output from an LLM involves providing two or three complete examples of correctly formatted manifests before stating the actual task?

Select one answer

Role prompting

Checklist prompting

Few-shot prompting

Skeleton with placeholders

Question 4

Before scaling a prompt-driven agentic workflow to production, which of the following should be true about your eval set? Select all that apply.

Select all that apply

At least three eval cases covering the main input variations

At least one eval case covering a refusal scenario

Eval cases stored in version control alongside the prompt definition

All eval cases must use exact string matching to verify output