Which process generates numbers that approximate randomness but are not truly random?

Which process generates numbers that approximate randomness but are not truly random?

• A. Entropy
• B. Certificate signing request (CSR)
• C. Pseudo RNG (PRNG)
• D. Common Name (CN)

Answer: (C)

Numbers that approximate randomness but aren’t truly random come from a pseudorandom number generator. It uses a deterministic algorithm and a seed to produce a long sequence of numbers that look random. Because the process is deterministic, the exact same seed and algorithm will always generate the same sequence, so given enough information you could predict future values. This makes them fast and reproducible, which is great for simulations or noncritical tasks, but not truly random. Entropy, in contrast, refers to the measure of unpredictability or randomness from a source, often used to seed generators, not a process that itself generates a sequence. The other two options—CSR and CN—relate to certificate management and naming within certificates, nothing to do with generating random numbers.

Numbers that approximate randomness but aren’t truly random come from a pseudorandom number generator. It uses a deterministic algorithm and a seed to produce a long sequence of numbers that look random. Because the process is deterministic, the exact same seed and algorithm will always generate the same sequence, so given enough information you could predict future values. This makes them fast and reproducible, which is great for simulations or noncritical tasks, but not truly random.

Entropy, in contrast, refers to the measure of unpredictability or randomness from a source, often used to seed generators, not a process that itself generates a sequence. The other two options—CSR and CN—relate to certificate management and naming within certificates, nothing to do with generating random numbers.