Crucial Exams

GCP Cloud Digital Leader Practice Question

A retail company wants to build an ML model on Google Cloud to forecast product demand. Their historical sales dataset contains many missing values and inconsistent product codes, but they plan to train the model without first cleaning the data. According to ML best practices, what is the most likely impact of proceeding with low-quality data?

  • Training will complete instantly because rows with errors are automatically skipped, so performance is unaffected.

  • The model's predictions will be less accurate because the learned patterns will be distorted by the poor data quality.

  • The model will appear accurate during training but will only fail when moved to Vertex AI for serving.

  • Google Cloud AutoML will compensate for data issues, guaranteeing high forecasting accuracy despite the inconsistencies.

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