Hackerrank Python Data Structures & Algorithms

Question 1

What is the time complexity of searching for an element in a Python dictionary?

Accepted Answer

O(1) average case

Answer

Python dictionaries use hash tables internally, providing O(1) average-case time complexity for lookups, insertions, and deletions. Worst case is O(n) due to hash collisions, but this is rare.

Question 2

What is the output of the following Python code?

my_list = [1, 2, 3, 4, 5]
print(my_list[1:4])

Accepted Answer

[2, 3, 4]

Answer

Python list slicing with [1:4] starts at index 1 (inclusive) and ends at index 4 (exclusive), returning elements at indices 1, 2, and 3, which are [2, 3, 4].

Question 3

Which Python data structure would be most efficient for implementing a FIFO (First-In-First-Out) queue?

Accepted Answer

collections.deque

Answer

collections.deque provides O(1) time complexity for both appending and popping from either end, making it ideal for FIFO queues. Lists have O(n) complexity for pop(0) operations.

Question 4

What does the Python `sorted()` function return when called on a dictionary?

Accepted Answer

A sorted list of the dictionary's keys

Answer

When sorted() is called on a dictionary, it iterates over the dictionary's keys (the default iteration behavior) and returns a new sorted list of those keys.

Question 5

What is a list comprehension in Python?

Accepted Answer

A concise syntax for creating lists by applying an expression to each item in an iterable

Answer

List comprehensions provide a compact way to create lists using the syntax [expression for item in iterable if condition], combining iteration, transformation, and optional filtering in a single line.

(Hackerrank) HackerRank Python Certification Practice Test

(Hackerrank) HackerRank Python Certification Practice Test

Hackerrank Python Data Structures & Algorithms