模型漂移检测测验

当您参加测验时，就会出现答案和解释。

1. 数据漂移和概念漂移有什么区别？
   • 数据漂移改变了输入的分布；概念漂移改变了输入与输出的关系
   • 它们是同一事物的两个名称
   • 数据漂移仅影响图像；概念漂移只影响文本
   • 训练时会发生数据漂移；测试中发生概念漂移
2. 团队应如何解释诸如 0.30 这样的 PSI 值？
   • As a guarantee that model accuracy has fallen
   • As a signal to investigate under the chosen monitoring policy, not a universal proof of harmful drift
   • As an identical threshold for every feature and population
   • As proof that retraining must begin immediately
3. 为什么基于输入的漂移检测可以提前预警？
   • 因为首先输入总是错误的
   • 因为输入是较小的文件
   • 因为真实标签通常比预测晚得多
   • 因为模型完全忽略标签
4. 双样本 Kolmogorov-Smirnov 检验比较什么？
   • The average accuracy of two classifiers only
   • The class labels predicted by a single model
   • The parameter values of two neural networks
   • The empirical cumulative distribution functions of two samples
5. 哪种情况是概念漂移的明显例子？
   • 即使对于外观相似的电子邮件，用户认为的“垃圾邮件”也会随着时间而变化
   • 相机产生稍微明亮的图像
   • 数据集中的客户年龄保持不变
   • 数据集以新的文件格式存储
6. What is a safe first response when an input-drift monitor triggers?
   • Automatically deploy a newly retrained model without evaluation
   • Investigate the data and impact, then decide whether retraining or another action is justified
   • Delete the reference data
   • Assume every shift is harmful concept drift
7. What can TensorFlow Data Validation check in an ML pipeline?
   • The causal effect of a feature
   • Whether every model prediction is correct
   • Data schemas and statistics, including unexpected changes
   • The absolute accuracy of an unlabeled dataset
8. What does the Population Stability Index (PSI) measure in a drift-monitoring setup?
   • The model’s accuracy on labeled examples
   • A causal effect of an input feature
   • A universal pass/fail threshold for all models
   • A numerical summary of how binned feature distributions differ between reference and current data