Meridian's predictive-maintenance team has three candidate data sources for forecasting component wear on its regional jet fleet: five years of structured sensor telemetry with consistent timestamps, two years of maintenance technician notes in free-text form, and a vendor's generic industry failure-rate table that isn't specific to Meridian's aircraft. During Data Understanding, how should the team evaluate which datasets are "appropriate" for this ML initiative?

Identifying appropriate datasets for machine learning means judging fitness for the specific business question, not just picking the largest or most convenient file. Relevance (does it capture the failure modes Meridian cares about), sufficient history (enough time span to see failure patterns), and traceability (is it actually Meridian's fleet, not a generic industry proxy) all matter more than row count. Ranking by size alone is the classic trap: five years of sensor data that measures the wrong variables is worse than two years that measures the right ones. Automatically discarding the free-text technician notes is also wrong — unstructured data is explicitly in scope for AI projects (extracting value from unstructured data is a core Data-for-AI competency), and technician notes often contain failure signals sensors miss. Adopting the vendor's generic industry table as the primary source is the weakest option of all: it isn't specific to Meridian's aircraft, maintenance practices, or operating environment, so a model trained primarily on it would generalize poorly to Meridian's actual fleet — it might be useful as a supplementary benchmark, but not as the backbone dataset. The exam expects PM-level judgment about fitness-for-purpose, not a data-science scoring formula.