Synthetic data is typically used in situations where:

Synthetic data is often employed in contexts where real-world data is sensitive or unavailable. This approach allows organizations to generate datasets that mimic the statistical properties of real data without compromising privacy or confidentiality. Utilizing synthetic data can help overcome restrictions related to data sharing, such as regulations like GDPR or HIPAA, where personal or sensitive information must be protected.

In scenarios where access to real-world data is limited due to scarcity, privacy concerns, or legal restrictions, synthetic data provides a viable alternative for training machine learning models, conducting research, and testing systems. By creating a dataset that replicates features from actual data while avoiding the ethical and legal issues associated with using real data, organizations can still conduct meaningful analytics and maintain compliance with data protection standards.

In contrast, situations where real-world data is abundant or where real-time data collection is feasible would not typically necessitate the use of synthetic data, as there would be sufficient access to genuine datasets for analysis. Similarly, while data uniqueness might be important for specific applications, it does not directly relate to the primary reasons synthetic data is created and utilized. Thus, the correct answer accurately reflects the primary utility of synthetic data in addressing the challenges posed by sensitive or unavailable real-world data.