You’re running an image recognition project and realize that you do not have enough data...

In CPMAI™ v7’s Phase III: Data Preparation, teams are instructed to construct the final modeling dataset through a variety of enhancement activities—including data augmentation, which specifically covers transforming existing records or generating entirely new records to increase volume and variety. This “augmentation” is described as “constructive data preparation operations such as the production of derived attributes or entire new records, or transformed values for existing attributes” .

Moreover, under the Training & Test Data Requirements task, the Workbook explicitly asks project teams to determine “What transformation or multiplication activities can be done to increase training data volume while maintaining quality” . Performing data transformation (e.g., image rotations, color jitter, cropping) and multiplication (synthetic record generation) directly addresses the lack of labeled samples without incurring the cost or delay of third-party purchases, making option B the correct approach.

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