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During the AI lifecycle, when should a 'Data Quality' assessment be performed to minimize long-term risk?
Data quality assessment must happen during data ingestion and preprocessing, because that is when data is collected, cleaned, validated, and transformed before it reaches model training. Catching quality issues here prevents garbage-in-garbage-out propagation and is far cheaper t…Read full explanation
Which AI lifecycle stage is most susceptible to the risk of 'data leakage' where training data inadvertently contains information from the future/target?
Data leakage occurs when information that should not be available at inference time (e.g., the target label, future data, or holdout data) is inadvertently included in the training set. This happens during data preparation and training, where feature engineering, joins, and train…Read full explanation
A practitioner is setting up a model monitoring service in AWS SageMaker Model Monitor. They observe that the ground truth data is significantly delayed. What action ensures risk identification remains effective?
When ground truth labels are delayed, SageMaker Model Monitor cannot compute model quality metrics (accuracy, precision, etc.) because those require labels. The effective fallback is to enable Data Quality Monitoring on input features, which detects drift, missing values, and dis…Read full explanation
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