AI 리드 점수 퀴즈

퀴즈를 풀면 정답과 설명이 나타납니다.

1. AI 리드 스코어링은 기존 규칙 기반 스코어링과 주로 어떻게 다릅니까?
   • 고정점을 사용하는 대신 과거 변환 데이터에서 가중치를 학습합니다.
   • 열린 이메일만 카운트됩니다
   • 데이터가 전혀 필요하지 않습니다
   • 회사 규모와 직위를 무시합니다.
2. 리드의 전환 여부 예측을 가장 잘 설명하는 머신러닝 작업은 무엇인가요?
   • 이미지 분할
   • 이진 분류
   • 음성 인식
   • 강화 학습
3. 리드 스코어링 모델을 평가할 때 일반 정확도가 좋지 않은 측정 기준인 이유는 무엇입니까?
   • 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
   • 하루 중 시간만
   • 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   • The original threshold can never become outdated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