在线和离线功能服务偏差测验

当您参加测验时，就会出现答案和解释。

1. 什么是训练/服务偏差？
   • 当模型在 GPU 上训练但在 CPU 上运行时
   • 当模型太大而无法容纳在内存中时
   • 当训练期间离线计算的特征与生产中在线提供的特征不同时
   • 当训练时间比预期长时
2. When building a historical training row, which feature timestamps should a point-in-time join allow?
   • Values available at or before that row’s prediction time
   • Values recorded only after the outcome
   • The latest values regardless of historical time
   • Only values from the next calendar month
3. 以下哪一项是导致生产中离线精度过高而崩溃的典型原因？
   • 使用的训练样本太少
   • 在比服务更快的机器上训练
   • 将特征存储在数据库中
   • 数据泄漏，训练使用预测时不可用的信息
4. A logged live feature distribution differs from training. What should the feature-serving team infer first?
   • The serving code is certainly defective
   • There is a mismatch to investigate; it may be drift or serving skew
   • The feature has no predictive value
   • The model must be running on different hardware
5. 为什么像“过去一小时的订单”这样的缓存在线功能存在偏差风险？
   • 缓存的值可能会过时，并且不再与训练中使用的新计算值匹配
   • 缓存总是可以提高模型的准确性
   • 缓存使得该功能无法离线计算
   • 缓存的特征不能是数字
6. Why share one feature definition across batch training and online serving?
   • To guarantee every cached value is fresh
   • To eliminate changes in incoming populations
   • To make timestamp checks unnecessary
   • To reduce differences caused by separate transformation implementations
7. A transaction model uses a chargeback flag recorded days after purchase. Why is its offline feature join invalid?
   • The flag is categorical rather than numerical
   • The flag was unavailable when the purchase prediction was made
   • Chargeback flags cannot be stored historically
   • Only GPU models may use transaction features
8. Training recomputes a one-hour order count, but serving reads a ten-minute-old cached count. Which check targets this skew?
   • Increase the number of model parameters
   • Remove all timestamps from the feature store
   • Compare counts for matching entities and times while recording cache freshness
   • Switch both paths to a different programming language