AI 线索评分测验

当您参加测验时，就会出现答案和解释。

1. 人工智能线索评分与传统的基于规则的评分有何主要区别？
   • 它从历史转换数据中学习权重，而不是使用固定点
   • 它只计算电子邮件打开次数
   • 它根本不需要任何数据
   • 它忽略了公司规模和职位名称
2. 哪项机器学习任务最能描述预测潜在客户是否会转化？
   • 图像分割
   • 二元分类
   • 语音识别
   • 强化学习
3. 为什么简单准确度对于评估潜在客户评分模型来说是一个糟糕的指标？
   • Accuracy directly measures calibration
   • Accuracy tells sales representatives why a lead will convert
   • Converters may be rare, so a model can appear accurate by predicting “no conversion” for nearly every lead
   • Accuracy automatically adjusts decision thresholds
4. According to HubSpot’s lead-scoring documentation, which data can a custom score use?
   • Only a person’s name
   • 仅一天中的某个时间
   • Unlabeled audio waveforms
   • Record actions or properties selected for the scoring model
5. What can a lead-scoring model return for each prospect?
   • A score or estimated conversion probability, which still needs evaluation
   • A probability that is automatically calibrated by definition
   • A guaranteed sales outcome
   • A legal determination of creditworthiness
6. What does feature importance summarize in a tree-based lead-scoring model?
   • That a feature causes conversion
   • How influential a feature is in the model’s decisions under that importance method
   • That a feature is legally required
   • The future sales quota
7. What does calibration mean for a lead-conversion score?
   • Every lead receives the same score
   • The model’s accuracy is always 100 percent
   • Predicted probabilities match observed conversion frequencies over comparable cases
   • Scores are sorted alphabetically by company
8. Why should a lead-scoring model be monitored after deployment?
   • The original threshold can never become outdated
   • Monitoring makes labels unnecessary
   • A model score permanently captures market behavior
   • Lead behavior and the meaning of historical patterns can drift