GCP Cloud Digital Leader Practice Question

Machine learning models discover patterns in the data they receive. When the training data is inaccurate or inconsistent, the model learns those errors as if they were true relationships, producing biased or noisy parameters. The result is lower prediction accuracy and unreliable business decisions. Training time, storage cost, and overfitting risk may change slightly, but none of those effects offset the core problem: poor-quality data leads directly to poor-quality models ("garbage in, garbage out"). Automated algorithms will not correct fundamental data errors on their own.