人工智能在销售测验中的应用

当您参加测验时，就会出现答案和解释。

1. A team uses a model to prioritize accounts. What should it define first?
   • The customer and business outcome the score is meant to support
   • How many messages the model can generate
   • The most active sales representative
   • 提示的长度
2. Why check which information was available before an outcome when building a lead score?
   • To guarantee every lead converts
   • To ensure the label reflects fit rather than past sales-team attention
   • To remove opt-out preferences
   • To improve message volume
3. A generated proposal includes a product feature and price. What should happen before sending it?
   • Add an urgency claim
   • Trust the model if it sounds confident
   • Verify both against current documentation
   • Send first and check later
4. Where should opt-out preferences remain enforceable?
   • In the model’s score explanation
   • Only in a prompt
   • Only in a generated draft
   • Outside the model, in the communication process
5. Which measures better reflect sales quality than message volume alone?
   • Total messages sent
   • Qualified opportunities, customer response, opt-outs, and satisfaction
   • Number of AI drafts
   • Length of the email sequence
6. Why evaluate model usefulness by segment?
   • Segment results cannot be measured
   • All accounts have identical data
   • Irrelevant outreach or weaker service may affect groups differently
   • It automatically increases conversion
7. A feature was discontinued, but the model recommends it. What should the team do?
   • Check the current product source and update the knowledge source
   • Send the proposal because it was generated
   • Change the lead score only
   • Assume the customer knows the feature is gone
8. What should be recorded for an important generated communication?
   • 生成的代币数量
   • Only the recipient’s name
   • Only the final send time
   • Model, sources, and human edits