For reliable model performance and meaningful comparisons across inputs, data consistency is essential.
Standardizing sensor data frequency and formats ensures that the model receives aligned time steps and coherent feature structures, reducing the risk of spurious patterns, missing signals, and biased predictions.
This is aligned with AAIA's focus on data quality, data balancing, and data preparation in AI Operations.
Option B ignores frequency and formatting differences, likely introducing noise and misalignment. Option C may sometimes be valid, but it increases complexity, maintenance overhead, and may still require consistent preprocessing pipelines. Option D addresses only one data source and does not solve the problem of heterogeneous sensor data. The most robust operational approach is to define clear data input requirements and standardize the sensor data (option A) before training.
References:
ISACA, AAIA Exam Content Outline - Domain 2: AI Operations (Data Management Specific to AI - data quality, data balancing, data security).
ISACA AI operations guidance on data pipelines and preprocessing for AI models.