Preparing Practice Exam…
Please wait a moment while the system generates the exam questions, answers, and study session.
Please wait a moment while the system generates the exam questions, answers, and study session.
Google Cloud Certified Professional Machine Learning Engineer · Google Cloud
Select one answer.
Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.
data leakege and NCV wont solve it
C. Address data leakage by removing features highly correlated with the target value.Why? Achieving 99% AUC ROC on training data with minimal effort is a red flag. The most likely cause is data leakage, where the model has access to information it shouldn't during training. Features that are highly correlated with the target often leak label information, leading to unrealistically high performance. ❌ Why not the others? A. Use a less complex algorithm: Overfitting might not be the issue here—your model is likely learning from leaked data, not overfitting due to complexity. B. Nested cross-validation: This helps with model selection and tuning, but it doesn't prevent leakage if the data itself is flawed. D. Tuning hyperparameters to reduce AUC ROC: You don’t tune to reduce performance; you tune to generalize better. High training AUC isn't the goal—generalization is.
Strangely high ROC => overly low bias/high variance =>data leakage. The time series nature of the data also points to data leakage as data leakage is more common in time series datasets. Now only B reference a method to reduce data leakage, i.e. NEsted Cross Validation which runs validation on multiple slices of data with different hyperparams (if I understood it correctly). Option C talks about removing highly correlated feature, but there isnt enough info that THAT is what is leading to data leakage. If the correlation is due to leakage , the correct fix is to re-engineer or remove the leaky component, not just remove every highly correlated feature.