Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
An organization is using Vertex AI Agent Builder to create a customer service agent. They want the agent to be able to hand off to a human agent when it cannot answer a question. What should they configure in the agent's design?
⚠ Common exam trap
Candidates may be tempted by the plausible-sounding 'Escalation' option, but the configurable mechanism for a human handoff when the agent cannot answer is the fallback/no-match path routed to a human.
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
✓
Use a 'Fallback' intent to route to a human
In Vertex AI Agent Builder (Dialogflow CX), when the agent cannot answer or match a user's request, the conversation triggers a fallback/no-match path. To hand off to a human, you configure that fallback path to route to a live agent via fulfillment, webhook, or live-agent handoff. There is no standard built-in 'Escalation' intent in the product; escalation is an outcome you implement, not a specific intent type.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure 'Slot filling' to collect more info
Why it's wrong here
Slot filling is for gathering parameters, not human handoff.
- ✗
Implement a 'Confirmation' prompt for the user
Why it's wrong here
Confirmation is for verifying actions, not handing off.
- ✗
Add an 'Escalation' intent that triggers a human handoff
Why it's wrong here
Escalation intent is designed for human handoff.
- ✓
Use a 'Fallback' intent to route to a human
Why this is correct
Fallback intent is for unrecognized inputs, not specifically for human handoff.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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