Google Cloud Professional Cloud Security Engineer Practice Exam

What method should be used to obfuscate start and end dates while preserving interval data in BigQuery?

1. Remove all date fields

2. Use random number generation for dates

3. Apply date shifting based on unique test subject IDs

4. Change date formats to text strings

The correct answer is: Apply date shifting based on unique test subject IDs

Using date shifting based on unique test subject IDs is the most appropriate method to obfuscate start and end dates while still retaining the integrity of interval data in BigQuery. This method allows for the modification of date values in a way that keeps the relative intervals intact—meaning the temporal relationships between the dates remain the same—even though the actual values are altered for privacy. By applying a unique shift to each subject’s dates, the original information is protected while the structure of the data (such as duration or intervals between dates) is preserved. This is crucial in many analysis scenarios where relationships in the data must be maintained for accurate results, such as in cohort analyses or longitudinal studies. Other methods may not effectively meet these requirements. For example, removing all date fields would eliminate any temporal data entirely, making it impossible to perform time-based analyses. Random number generation for dates might create values that change the intervals between dates significantly, thereby losing meaningful data relationships. Changing date formats to text strings could lead to complications in performing time-based functions or analyses within BigQuery, as it would no longer be handled as date data. Hence, date shifting not only secures sensitive information but also retains essential analytical capabilities, making it the best choice in this context.