AI Onibara Service adanwo

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. Why define resolution separately for password resets, billing disputes, and safety issues?
   • Different request types need different evidence and escalation
   • Every request should use the same answer
   • It makes automation rate higher
   • It removes privacy requirements
2. A customer asks for an address change. What should the system require?
   • A plausible-sounding request
   • Authorization, validation, and verification
   • A fast sentiment score
   • A generated apology
3. A refund tool times out after possibly creating a refund. What should happen before retrying?
   • Tell the customer it is complete
   • Retry the refund immediately
   • Look up the transaction identifier to avoid a duplicate
   • Delete the conversation
4. What does idempotent handling help prevent?
   • A human handoff
   • Incorrect language detection
   • Long wait times in every case
   • Duplicate operations when a request is retried
5. Why is automation rate an incomplete customer service metric?
   • It always measures customer satisfaction
   • A fast automated interaction may not resolve the issue
   • It guarantees first-contact resolution
   • It captures accessibility needs
6. How should service quality be broken down during evaluation?
   • Only by the model version
   • Only by total volume
   • By language, accessibility needs, and issue type
   • By the agent’s preferred color
7. What should be preserved during a human handoff?
   • Conversation context and the customer’s issue history
   • Only the last message
   • The model’s hidden reasoning
   • No records of the interaction
8. What should a service team maintain when a model or provider fails?
   • A longer waiting message with no action
   • A higher automation target
   • A second unreviewed model only
   • A usable manual path