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GCP Cloud Digital Leader Practice Question

A retailer wants to build a demand-forecasting model but discovers that its historical sales data contains many missing values and duplicate records. From a machine-learning perspective, why is it critical to fix these data quality issues before training begins?

  • Models learn patterns present in the training data, so inaccurate or inconsistent data will lead the model to make unreliable forecasts in production.

  • Using cleaner data eliminates the need for specialized compute hardware such as GPUs during model training.

  • Regulations require all machine-learning datasets to be perfectly balanced, so correcting data issues is mandatory for legal compliance.

  • Cloud Storage applies higher charges to datasets that contain errors, so cleaning the data avoids extra cost.

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