ISC2 Certified Secure Software Lifecycle Professional (CSSLP) Practice Question
Your team builds a synthetic customer-transaction dataset for load testing to avoid exposing regulated production records. Before release, you must prove the set keeps key statistics such as purchase-amount distributions and null-field rates. Which action best verifies the dataset's statistical quality?
Replace sensitive attributes with a single constant and execute functional regression tests.
Tokenize all primary key columns so no real identifiers remain in the dataset.
Run a dynamic application security test (DAST) against the application that will use the dataset.
Apply a chi-square goodness-of-fit test to compare each synthetic field's frequency distribution with production data.
Statistical quality is shown by testing whether value frequencies in the synthetic set match those in production. A chi-square goodness-of-fit test does this by comparing observed versus expected counts and reporting a p-value for significant differences. Security scans, constant masking, or tokenizing keys improve confidentiality or functionality but do not measure representativeness of numeric or categorical distributions.
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ISC2 Certified Secure Software Lifecycle Professional (CSSLP)
Secure Software Testing
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