Random Number Generation Concepts

Random Number Generation Concepts

Assessment

Interactive Video

Computers

9th - 12th Grade

Practice Problem

Hard

Created by

Amelia Wright

FREE Resource

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.

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10 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

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

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

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

math

random

numpy

statistics

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

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

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

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

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

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

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

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

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

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

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