Unit 2 computing

Question 1

What is caching?

Answer 1

Predicting the data that will be needed later on in the program and storing it in cache (faster to access)

Question 2

What is prefetching?

Answer 2

Where data is requested from main memory before it’s actually required

Question 3

What are the benefits of caching?

Answer 3

Improves speed and efficiency of processing

Question 4

What are the drawbacks of caching?

Answer 4

Complexity, wrong data can be cached and has to be flushed

Question 5

Can reusing code be a positive?

Answer 5

Saves time and resources

Question 6

What is recursion?

Answer 6

When a subroutine calls itself within its own subroutine

Question 7

What are the 3 characteristics of a recursive subroutine?

Answer 7

Stopping condition, routine calls itself for any value except stopping condition, stopping condition reachable within finite number of times

Question 8

What does IDE stand for?

Answer 8

Integrated development environment

Question 9

What is the purpose of an IDE?

Answer 9

To provide a range of tools and features that help speed up and enhance program development

Question 10

What are examples of features in an IDE?

Answer 10

Code editors, error diagnostics, run-time environments, translators and auto-documentation

Question 11

What is an example of an IDE?

Answer 11

Python IDLE (Integrated Development and Learning Environment)

Question 12

What is a run-time environment?

Answer 12

Software that supports the running of programs allowing programmers to easily run code during development

Question 13

What is done in auto-documentation?

Answer 13

Variables and modules are tracked with a view to produce documentation that aids in program maintenance, debugging and support

Question 14

What are the two rules of Big O notation?

Answer 14

Remove all terms except one with largest exponent, remove any constant from this

Question 15

What does O(1) mean in Big O notation?

Answer 15

Constant - always executes in same amount of time regardless of data set

Question 16

What does O(log n) mean in Big O notation?

Answer 16

Logarithmic - complexity grows slowly in proportion to number of items in list

Question 17

What does O(n) mean in Big O notation?

Answer 17

Linear - direct correlation between input size and time taken

Question 18

What does O(n²) mean in Big O notation?

Answer 18

Polynomial - impact of algorithm is directly proportional to the square of the input size

Question 19

What does O(2ⁿ) mean in Big O notation?

Answer 19

Exponential - time taken to execute algorithm doubles with each item added

Question 20

What characteristics will an O(1) algorithm have?

Answer 20

No loops or iterations

Question 21

What characteristics will an O(log n) algorithm have?

Answer 21

Halving data set

Question 22

What characteristics will an O(n) algorithm have?

Answer 22

A singular for or while loop

Question 23

What characteristics will an O(n²) algorithm have?

Answer 23

Nested loops

Question 24

What characteristics will an O(2ⁿ) algorithm have?

Answer 24

Recursion (if it calls itself twice)

Question 25

How do you execute a pre-order tree traversal?

Answer 25

(node-left-right) Output root, mark count on left of each node when drawing traversal

Question 26

How do you execute an in order tree traversal?

Answer 26

(left-node-right) mark count on bottom of each node when drawing traversal

Question 27

How do you execute a post order tree traversal?

Answer 27

(left-right-node) mark count on right of each node when drawing traversal

Question 28

What is a linked list?

Answer 28

A list which provides a foundation for other structures to be built

Question 29

How does a linked list work?

Answer 29

Each node has a pointer indicating to other nodes in the linked list

Question 30

What does the pointer point to when there are no more items in a linked list?

Answer 30

Null

Question 31

When would you pass values in a function by value rather than by reference?

Answer 31

When the original values don’t need to be changed