Exploring Searching Algorithms

Exploring Searching Algorithms

11th Grade

15 Qs

quiz-placeholder

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Exploring Searching Algorithms

Exploring Searching Algorithms

Assessment

Quiz

Other

11th Grade

Hard

Created by

Gusty De Yos

FREE Resource

15 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is a searching algorithm?

A searching algorithm is a method for deleting items from a data structure.

A searching algorithm is a method for finding a specific item in a data structure.

A searching algorithm is a way to sort data in a structure.

A searching algorithm is a technique for compressing data.

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Name two types of searching algorithms.

Linear Search, Binary Search

Jump Search

Breadth-First Search

Depth-First Search

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the time complexity of linear search?

O(n^2)

O(1)

O(log n)

O(n)

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How does binary search work?

Binary search efficiently finds a target value in a sorted array by dividing the search interval in half.

Binary search requires the array to be in descending order.

Binary search works by searching through an unsorted array.

Binary search finds the target by checking each element sequentially.

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the prerequisite for using binary search?

The data must be unsorted.

The data must be in binary format.

The data must be sorted.

The data must be in ascending order.

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Explain the difference between linear search and binary search.

Linear search is sequential and works on any list; binary search is faster, requires a sorted list, and divides the search space in half.

Binary search works on any list and is sequential.

Linear search is faster and requires a sorted list.

Linear search divides the search space in half.

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the worst-case time complexity of binary search?

O(1)

O(n log n)

O(log n)

O(n)

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