Random Number Generation Concepts

The video explores random number generation in computer science, highlighting its applications in various fields such as simulations, cryptography, and lotteries. It demonstrates how to generate random numbers using Python and explains the difference between pseudorandom and true random numbers. The video also delves into the concept of entropy and how it is used to generate true random numbers, emphasizing the challenges of achieving true randomness in deterministic systems like computers.

Which of the following is NOT an example of a program that might need to generate random numbers?

• A quiz program

• A text editor

• A national lottery program

• A dice simulation

In Python, which library is used to generate random numbers?

• math

• random

• numpy

• statistics

What are the two parameters required by the random function in Python?

• The seed value and the range

• The lowest and highest values that can be generated

• The number of random numbers to generate and the range

• The type of random number and the range

What is the main difference between pseudorandom numbers and true random numbers?

• Pseudorandom numbers are used in cryptography, true random numbers are not

• Pseudorandom numbers are always predictable, true random numbers are not

• Pseudorandom numbers are generated by algorithms, true random numbers are not

• Pseudorandom numbers are generated by hardware, true random numbers are generated by software

Why do pseudorandom number generators need a seed value?

• To increase the speed of random number generation

• To reduce the memory usage of the program

• To alter the output list of random numbers each time the algorithm is run

• To ensure the numbers are truly random

What is the Unix epoch time?

• The number of hours since January 1st, 1970

• The number of seconds since January 1st, 1970

• The number of minutes since January 1st, 1970

• The number of milliseconds since January 1st, 1970

What is entropy in the context of generating true random numbers?

• A measure of the randomness gathered from the physical world

• A measure of the speed of random number generation

• A measure of the memory usage of the random number generator

• A measure of the predictability of a system

Which of the following is an example of gathering entropy for true random number generation?

• Using a fixed algorithm

• Generating numbers based on the current time

• Measuring the radioactive decay of an atom

• Using a pseudorandom number generator

What is the function called on Linux systems to generate truly random numbers?

• dev/random

• dev/urandom

• random/dev

• urandom/dev

Why is it difficult for computers to generate truly random numbers?

• Because computers do not have enough processing power

• Because computers cannot access the physical world

• Because computers are deterministic and follow fixed algorithms

• Because computers are inherently unpredictable