The best answer is B. Utilizing data interpolation methods. In PMI-CPMAI, data readiness depends on whether the data is suitable for the intended AI use case, including whether it meets requirements for sampling strategy, temporal alignment, granularity, and consistency. PMI's exam outline specifically highlights determining sampling strategies and temporal requirements, assessing data quality dimensions such as accuracy, completeness, and consistency, and validating preprocessing and transformation results before making a go/no-go decision for model development.

When multiple sensors produce data at different sampling rates, interpolation is a common and effective way to align measurements onto a consistent timeline so that downstream models can learn from synchronized inputs. This is the strongest choice because it directly addresses the inconsistency created by mismatched sensor frequencies. A custom integration framework may be useful technically, but it does not by itself solve the consistency problem. Real-time synchronization protocols are more relevant to live acquisition architecture and may not be feasible or necessary during data preparation. Simple aggregation may reduce detail and distort patterns that are important for model training. Under PMI-CPMAI logic, the most appropriate action is the one that best preserves usable, comparable data while supporting a rigorous data-quality decision.