Guide-linked quiz · Medium Level
AI Workflow Automation Quiz
Tests workflow boundaries, semantic validation, authorization, retries, partial completion, approvals, and end-to-end outcomes.
Question 1 of 8
When mapping an automated workflow, which parts should be marked separately?
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Answers and explanations appear as you take the quiz.
- When mapping an automated workflow, which parts should be marked separately?
- Only the model prompt and vendor
- Trigger, inputs, decision points, actions, and completion condition
- Only the first and last screen
- The number of steps that use AI
- A model classifies an invoice, and code sends payment. What validation is needed before payment?
- Check only that the response is valid JSON
- Ask the same model whether it is safe
- Check the schema and meaning, including account, amount, destination, and authorization
- Trust the classification label as permission
- A request times out after an external action may have succeeded. What should a retry do?
- Repeat immediately because there was no response
- Assume the operation definitely failed
- Start a duplicate workflow under a new identifier
- Reconcile durable state and operation identifiers before repeating side effects
- What does a valid structured model output establish?
- That it meets a format contract, not that its meaning or scope is correct
- That the action is authorized
- That all values match source records
- That downstream execution is safe
- Why narrow the model-dependent step in an automation?
- It removes the need for retry handling
- A smaller uncertain part is easier to test and contain
- It guarantees that the model will not fail
- It makes permissions unnecessary
- A workflow partially completes before a timeout. Which design helps avoid duplicate effects?
- Randomly changing the identifier on every retry
- Discarding all completion records
- Durable state and stable operation identifiers
- Using a longer model prompt
- What makes human approval useful for a proposed automated action?
- A single approve button with no details
- A notification after the action has run
- A model-generated claim that the action is low risk
- Enough context for the reviewer to assess the action and its scope
- Which outcome better measures automation quality than step count?
- Correct completed workflows and manual recovery burden
- Number of model calls
- Number of automated steps executed
- Average prompt length
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