Fundamental Principles and Concepts of Computing

Question 1

a set of well-defined instructions for performing a task or solving a problem.

Answer 1

Algorithm

Question 2

Uses divide-and-conquer to sort elements by partitioning arrays.

A. Algorithm
B. QuickSort
C. Binary Search
D. MergeSort

Answer 2

B. QuickSort

Question 3

Divides the list into halves, sorts them, and merges them.

A. Algorithm
B. QuickSort
C. Binary Search
D. MergeSort

Answer 3

D. MergeSort

Question 4

Efficiently finds an element in a sorted list by repeatedly dividing the search interval in half.

A. Algorithm
B. Linear Search
C. Binary Search
D. MergeSort

Answer 4

C. Binary Search

Question 5

Checks each element in a list until the target is found.

A. Algorithm
B. Linear Search
C. Binary Search
D. MergeSort

Answer 5

B. Linear Search

Question 6

Store elements of the same type & used Ideally for indexing and iterating over elements

A. Stacks
B. Arrays
C. Linked Lists
D. Queues

Answer 6

B. Arrays

Question 6

Specialized formats for organizing, processing, retrieving, and storing data.

Answer 6

Data Structure

Question 7

Efficient for insertions and deletions.

A. Stacks
B. Arrays
C. Linked Lists
D. Queues

Answer 7

C. Linked Lists

Question 8

Elements are added and removed from the same end & used for undo mechanisms in applications, parsing expressions.

A. Stacks
B. Arrays
C. Linked Lists
D. Queues

Answer 8

A. Stacks

Question 9

Elements are added at one end and removed from the other & task scheduling, managing requests.

A. Stacks
B. Arrays
C. Linked Lists
D. Queues

Answer 9

D. Queues

Question 10

Hierarchical data representation, search operations.

A. Queues
B. Trees
C. Graphs
D. Hash Tables

Answer 10

B. Trees

Question 11

Consist of nodes (vertices) and edges (links) & represents networks, relationships, and dependencies.

A. Queues
B. Trees
C. Graphs
D. Hash Tables

Answer 11

C. Graphs

Question 12

fast access to data

A. Queues
B. Trees
C. Graphs
D. Hash Tables

Answer 12

D. Hash Tables

Question 13

Study of the amount of resources required by an algorithm to run, typically time and space.

Answer 13

Computational Complexity

Question 14

Describes the upper bound of an algorithm’s running time or space requirements in terms of input size, helping to classify algorithms by their efficiency.

Answer 14

Big O Notation

Question 15

Type of Big O Notation: regardless of input size.

A. Constant time [O(1)]
B. Linear time [O(n)]
C. Logarithmic time [O(log n)]
D. Quadratic time [O(n^2)]

Answer 15

A. Constant time [O(1)]

Question 16

Type of Big O Notation:
grows proportionally with input size.

A. Constant time
B. Linear time
C. Logarithmic time
D. Quadratic time

Answer 16

B. Linear time

Question 17

Type of Big O Notation: increases slowly with
input size.

A. Constant time
B. Linear time
C. Logarithmic time
D. Quadratic time

Answer 17

C. Logarithmic time

Question 18

Type of Big O Notation:
common in simple sorting
algorithms like bubble sort.

A. Constant time
B. Linear time
C. Logarithmic time
D. Quadratic time

Answer 18

D. Quadratic time

Question 19

Breaks the Main Task into a smaller task

A. Abstraction
B. Pattern Recognition
C. Decomposition

Answer 19

C. Decomposition

Question 20

Focusing on the essential details while ignoring the irrelevant ones.

A. Abstraction
B. Pattern Recognition
C. Decomposition

Answer 20

A. Abstraction

Question 21

Identifying patterns to simplify complex problems.

A. Abstraction
B. Pattern Recognition
C. Decomposition

Answer 21

B. Pattern Recognition