MLS-C01 Question #378: Real Exam Question with Answer & Explanation

Question

A data scientist uses Amazon SageMaker Data Wrangler to obtain a feature summary from a dataset that the data scientist imported from Amazon S3. The data scientist notices that the prediction power for a dataset feature has a score of 1. What is the cause of the score?

Options

• ATarget leakage occurred in the imported dataset.
• BThe data scientist did not fine-tune the training and validation split.
• CThe SageMaker Data Wrangler algorithm that the data scientist used did not find an optimal model
• DThe data scientist did not process the features enough to accurately calculate prediction power.

Explanation

A prediction power score of 1 for a feature in Amazon SageMaker Data Wrangler's summary indicates that target leakage has occurred in the dataset.

Common mistakes.

• B. An improper training and validation split might affect overall model evaluation but does not directly cause an individual feature's prediction power to be 1 in a feature summary.
• C. The SageMaker Data Wrangler quick model's inability to find an optimal model pertains to the model's performance, not an individual feature's intrinsic prediction power score of 1, which typically points to a data issue.
• D. Insufficient feature processing would more likely lead to lower prediction power or difficulty for a model to learn, rather than a perfect prediction power score of 1 for a feature.

Concept tested. Target leakage in feature engineering

Reference. https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-troubleshoot.html

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