Meridian's early predictive-maintenance model performs poorly, and investigation reveals the training data included maintenance logs with inconsistent units, missing timestamps, and mislabeled failure events. Which principle explains why the model's poor performance traces directly back to this training data?

Garbage in, garbage out is the principle that a model's output quality is fundamentally bounded by its input data quality — inconsistent units, missing timestamps, and mislabeled failure events in Meridian's maintenance logs will degrade predictions no matter how sophisticated the chosen algorithm is. This is a core, explicitly named risk in the Data for AI domain and a common cause of underperforming pilot projects. The Turing Test is an unrelated AI-fundamentals concept about human-indistinguishability of output, not a data-quality principle. The seven patterns of AI concerns matching a business need to a use-case pattern, such as conversational or predictive, not diagnosing why a model underperforms due to bad training data. The data-to-wisdom pyramid, sometimes called DIKUW, describes the progression from data to wisdom used in Business Understanding scoping, not a data-quality diagnostic. For the exam, garbage in, garbage out is the go-to answer whenever a scenario traces poor model performance specifically back to flawed input data rather than model choice — a frequently tested root-cause pattern.