General Topics

Question 1

What are the core tools for solving coding interview problems?

Answer 1

Data Structures and Algorithms

Question 2

Questions to keep in mind when reading a coding interview problem:

Answer 2

• What is the input?
• What is the output?
• What is the ask?
• What transformation is needed?

Question 3

What is a Data Structure (General Definition)?

Answer 3

A way to organize and manage data

Question 4

What is a Data Structure (Precise Definition)?

Answer 4

The collection of 1) data values, 2) the relationships among those values and 3) the operations/functions that can be performed on those values.

Question 5

What is Complexity Analysis

Answer 5

The process of determining how efficient and algorithm is.

Question 6

What are the two types of complexity?

Answer 6

Time and Space.

Question 7

What do we use Complexity Analysis for?

Answer 7

It is how we can measure how “good” an algorithm is compared to other algorithms.

Question 8

What is Time Complexity?

Answer 8

A measure of how fast and algorithm runs.

Question 9

What is Space Complexity?

Answer 9

A measure of how much auxilliary memory and algorithm takes up.

Question 10

What is Big O notation?

Answer 10

Notation for describing time complexity and space complexity of algorithms.

Question 11

Bit

Answer 11

Binary Digit

Question 12

What is the fundamental unit of information in Computer Science?

Answer 12

Bit. Any data store in a computer is composed of bits.

Question 13

What values can a bit be?

Answer 13

Either 0 or 1.

Question 14

Byte

Answer 14

A grouping of 8 bits.

Question 15

How many bits (or data values) can a byte represent?

Answer 15

If unsigned, 0 to 255 or 2^8 (staring at 0). If signed, -128 to 127 or -2^7 to (2^7 - 1)

Question 16

Fixed-width Integer

Answer 16

An integer represented by a fixed number of bits, regardless of the value of the integer.

Question 17

What does “O” stand for in “Big O notation?”

Answer 17

Order. It represents the order (i.e. rate) of growth of an algorithm’s running time and/or space requirements in relation to the size of the input.

Question 18

What scenario of algorithm performance does Big O notation aim to emulate.

Answer 18

Worst-case or upper-bound performance.

Question 19

What does “n^2” stand for in “O(n^2)?”

Answer 19

The running time (and/or space depending on what kind of complexity we are analyzing for) of the algorithm grows proportionally to the square of the input size.

Question 20

How many complexities am I responsible for knowing for my coding interviews?

Answer 20

8

Question 21

How many categories of complexities am I responsible for know in my coding interviews?

Answer 21

4

Question 22

What are the categories of complexities I am responsible for knowing in my coding interviews?

Answer 22

Linear, Logarithmic, Exponential, Factorial

Question 23

What are the Linear Complexities?

Answer 23

Constant and Linear

Question 24

What are the Logarithmic Complexities?

Answer 24

Logarithmic and Log-Linear

Question 25

What are the Exponential Complexities?

Answer 25

Quadratic, Cubic and Exponential

Question 26

What complexity is the sole member of the Factorial category?

Answer 26

Factorial

Question 27

What is the Big O notation for Constant complexity?

Answer 27

O(1) What

Question 28

What is the Big O notation for Linear complexity?

Answer 28

O(n)

Question 29

What is the Big O notation for Logarithmic complexity?

Answer 29

O(log(n))

Question 30

What is the Big O notation for Log-Linear complexity?

Answer 30

O(nlog(n))

Question 31

What is the Big O notation for Quadratic complexity?

Answer 31

O(n^2)

Question 32

What is the Big O notation for Cubic complexity?

Answer 32

O(n^3)

Question 33

What is the Big O notation for Exponential complexity?

Answer 33

O(2^n)

Question 34

What is the Big O notation for Factorial complexity?

Answer 34

O(n!)

Question 35

What do the variables (usually n) in Big O notation represent?

Answer 35

The size of the inputs into algorithms

Question 36

What are the adjacent complexities of Constant

Answer 36

O(1) is the fasted of the set that I'm responsible knowing: O(log(n)) -> O(1)

Question 37

What are the adjacent complexities of Logarithmic

Answer 37

O(n) -> O(log(n)) -> O(1)

Question 38

What are the adjacent complexities of Linear

Answer 38

O(nlog(n)) -> O(n) -> O(log(n))

Question 39

What are the adjacent complexities of Log-Linear

Answer 39

O(n^2) -> O(nlog(n)) -> O(n)

Question 40

What are the adjacent complexities of Quadratic

Answer 40

O(n^3) -> O(n^2) -> O(nlog(n))

Question 41

What are the adjacent complexities of Cubic

Answer 41

O(2^n) -> O(n^3) -> O(n^2)

Question 42

What are the adjacent complexities of Exponential

Answer 42

O(n!) -> O(2^n) -> O(n^3)

Question 43

What are the adjacent complexities of Constant

Answer 43

O(n!) is the slowest algorithm that I'm responsible for knowing: O(n!) -> O(2^n)

Question 44

In coding interviews, what is the implied base of the logarithm?

Answer 44

2

Question 45

Explain O(log(n)) in plain English.

Answer 45

Every time the log input doubles, the number of operations need to process that input one increases by one unit.

Question 46

What are the two rules of thumb that indicate a complexity of O(log(n))

Answer 46

1) You are removing half of your inputs for every step or 2) Doubling the inputs only results in one extra step in the algorithm.