quiz bu lëkkale ak tegtal · Diggante Niveau

Quiz buy joxe poñ ci IA

Saytu ni ñuy jëfandikoo poñ yi ñuy jëfandikoo ci jaar-jaar, lu tax désequilibre ci klaas yi di am njeexital ci jàngat bi, ak lu tax etalonnage ak di wottu derive dañu am solo.

Yooni njiit yu jëm ciIA Lead Scoring
Laaj 1 de 8

Lan moo wuutale poñ yiy jiite IA ak poñ yi ñuy faral di def ci sàrt yi?

Xoolaatal laaj bu nekk8 laaj

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

  1. Lan moo wuutale poñ yiy jiite IA ak poñ yi ñuy faral di def ci sàrt yi?
    • Dafay jàng ay poid ci done yuñ soppi ci jamono, du jëfandikoo ay poñ yu takku
    • Dafay lim rek imeel yi ubbeeku
    • Soxlawul benn done
    • Du bàyyi xel ci dayo liggéeyukaay bi ak turu liggéey bi
  2. Ban liggéeyu jàngu masin moo gëna mëna wax luy waaja am ndax prospect bi dina soppi?
    • Segmentasioŋ nataal
    • Classification binéer
    • Xamme kàddu
    • Jàng buy dooleel
  3. Lan moo waral jaar-jaar bu leer nekkul jangat bu baax ngir jàngat xeetu poñ yi?
    • 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
    • Waxtu bis bi kese
    • 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