Had something like this in a mock, it's A for time series so you don't leak future info into training.

D makes sense, but what if users say something unexpected that's not a direct synonym? Sometimes Lex can't generalize unless synonyms are mapped specifically. In similar exam questions, AWS is picky about using synonyms over creating extra slot types or adding to enumeration values. I think D is right, but it really depends on how you anticipate user input. Someone disagree?

B D, and E are right if they're specifically asking about feature selection techniques. But if the question meant features to help normalize or preprocess data instead of just select important ones, then A might be in play too. Does the question require picking the most useful for selection specifically or is it about general prep for ML?

Option D is the way to go. Retraining with original data plus new data is key since user behavior and inventory shift over time. B is a trap here, just tuning hyperparams won't fix stale training data. Seen similar on other practice sets.

I see why A might work since you can target the notebook ARN in a bucket policy, and that would technically allow access. But if the bucket is already locked down by VPC or has multiple principals, edge cases could make this less secure. Picking A.

I don’t think it’s B. Multiple imputation (C) is more robust here since it uses other columns to estimate missing values, which helps maintain statistical integrity. Last observation carried forward works best for time series but not general datasets like this.

D imo. Random Cut Forest is used for anomaly detection, which sounds like it could work for fraud. But does the question say if they're expected to use supervised labels or just find outliers? If it wasn't labeled data, I'd definitely pick D instead of B here.