The best answer is A. Using generative AI (GenAI) to create additional relevant data . PMI’s official CPMAI exam content outline specifically includes supervising data augmentation and synthetic data generation within the data-preparation responsibilities of an AI project professional. That makes this choice the clearest PMI-aligned answer when the goal is to increase data quantity in a controlled way because information is missing or insufficient. Generative AI can help create additional relevant synthetic examples that support model development, provided the team also validates quality, documents transformations, and manages bias carefully.

The other options do not directly address the stated objective. Responsible AI techniques are important for governance and ethics, but they do not themselves augment the data set. Rule-based filtering may clean or reduce data, not increase it. Sentiment analysis is a modeling technique for a particular kind of text problem and is unrelated to filling data shortages in general. PMI’s broader trustworthy AI guidance also stresses that synthetic or augmented data must be handled responsibly so that teams do not introduce new distortions while trying to solve a quantity problem. That is why GenAI-based creation of additional relevant data is the strongest answer, as long as it is paired with validation and bias controls.