Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Idanwo Asiwaju AI

Assess how lead scoring uses historical outcomes, why class imbalance affects evaluation, and why calibration and drift monitoring matter.

Jẹmọ itọsọna awọn ọnaIfimaaki asiwaju AI
Ibeere 1 ti 8

Bawo ni igbelewọn asiwaju AI ni akọkọ ṣe yatọ si igbelewọn ti o da lori ofin ibile?

Awotẹlẹ gbogbo ibeere8 ibeere

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

  1. Bawo ni igbelewọn asiwaju AI ni akọkọ ṣe yatọ si igbelewọn ti o da lori ofin ibile?
    • O kọ awọn iwuwo lati data iyipada itan dipo lilo awọn aaye ti o wa titi
    • O ka awọn ṣiṣi imeeli nikan
    • Ko nilo data rara
    • O foju iwọn ile-iṣẹ ati akọle iṣẹ
  2. Iru iṣẹ ikẹkọ ẹrọ wo ni o ṣe apejuwe asọtẹlẹ boya asiwaju yoo yipada?
    • Pipa Pipa
    • Ipinsi alakomeji
    • Idanimọ ọrọ
    • Ẹkọ imudara
  3. Kini idi ti išedede pẹtẹlẹ jẹ metiriki ti ko dara fun iṣiroyewo awọn awoṣe igbelewọn asiwaju?
    • 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
    • Nikan akoko ti ọjọ
    • 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    • The original threshold can never become outdated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