What practices illustrate effective test data management?

• A. Ensure realistic, representative data with masking; use data subsets; maintain data integrity across environments; document data creation.
• B. Use only production data without masking.
• C. Create synthetic data that has no relation to real data.
• D. Delete data after tests.

Answer: (A)

Managing test data well means balancing realism with privacy and reproducibility. Realistic, representative data lets tests exercise real-world scenarios, data distributions, and how systems interact, so results reflect what actually happens in production. Masking protects sensitive information, so you can reuse production-like data safely in test environments without exposing PII or confidential fields. Using data subsets keeps tests fast and focused while still covering key cases and patterns. Maintaining data integrity across environments ensures that data shapes, relationships, and constraints stay consistent from development through test to staging, preventing environment-specific surprises. Documenting how data is created, masked, and refreshed gives you a clear, repeatable workflow that supports debugging, audits, and onboarding.

Using production data without masking is unsafe and often noncompliant. Synthetic data that has no relation to real data may miss important patterns and interactions. Deleting data after tests can hinder debugging and the ability to reproduce issues later.