Secure Test Data CSSLP Practice Questions (Page 6)

Questions 26–30

1. 26application · medium

   A healthcare application has a relational database with a `Patients` table and an `Appointments` table. The QA team is generating synthetic test data. They need every appointment to reference a valid patient ID, and they also want to ensure that if a patient record is updated, the corresponding appointments still reference the same patient. Which approach should they use?

   • AUse a single sequence for both patient IDs and appointment IDs so they never collide.
   • BGenerate patient IDs and appointment records independently, then use a script to fix any orphaned references.
   • CInsert appointments with placeholder patient IDs and update them later with real IDs from production.
   • DGenerate patient records first, then generate appointments using the actual patient IDs that were created.
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2. 27foundation · easy

   What is a key consideration when using data aggregation for test data?

   • AAggregated data is always fully anonymized
   • BAggregated data is always larger than the original data
   • CAggregated data may not support testing of individual record-level logic
   • DAggregated data requires more storage than raw data
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4. 28foundation · easy

   How does tokenization maintain referential integrity in test data?

   • ABy deleting foreign key constraints during tokenization
   • BBy using the same token for the same original value across all tables
   • CBy assigning a random token to every row regardless of the original value
   • DBy encrypting the tokens with a shared key
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5. 29application · medium

   A retail company needs to test a new inventory management system. They want test data that behaves like production data—including realistic product categories, price ranges, and stock levels—but they are prohibited from using actual production data due to privacy policies. Which approach should they take?

   • ACopy production data to a secure test server and restrict access.
   • BGenerate data with random values for every field to ensure uniqueness.
   • CUse a subset of production data and remove all product names.
   • DCreate synthetic data based on production data profiles, such as category distributions and price ranges.
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6. 30application · medium

   A research organization wants to share customer purchase data with a university for testing a new analytics tool. The data includes individual transactions, but the organization is concerned about re-identification. They decide to aggregate the data by region and product category. What is the primary benefit of this approach?

   • AIt reduces the risk of re-identification by lowering the granularity of the data.
   • BIt ensures the data is fully anonymized and irreversible.
   • CIt makes the data more useful for testing individual-level behavior.
   • DIt eliminates the need for any other privacy controls.
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