Advanced Competitive Programming Interview Test (20 Questions)
1. Which of the following time complexities represents the most efficient algorithm?
A) O(n²)
B) O(2ⁿ)
C) O(log n)
D) O(n log n)
2. What will be the output of the following code?
3. Write a function to find the longest increasing subsequence in an array using dynamic programming.
4. Explain the difference between BFS and DFS in graph traversal, and when each is preferred.
5. Given a sorted array of integers, which algorithm can be used to find a target value in O(log n) time?
A) Linear search
B) Binary search
C) Bubble sort
D) Merge sort
6. What is the time complexity of the following code snippet?
7. Write a program to implement Dijkstra's algorithm for finding the shortest path in a weighted graph.
8. What does the term "greedy choice property" refer to in greedy algorithms?
9. Which data structure is most suitable for implementing a priority queue efficiently?
A) Stack
B) Queue
C) Binary heap
D) Linked list
10. What will be the output of this code?
11. Implement a recursive function to compute the nth Fibonacci number with memoization.
12. Describe the concept of divide and conquer with an example.
13. What is the space complexity of quicksort in the worst case?
A) O(1)
B) O(log n)
C) O(n)
D) O(n²)
14. Write a function that checks whether a given string is a palindrome using recursion.
15. In the context of competitive programming, what is the purpose of using bit manipulation?
16. What will be the output of the following code?
17. Design a solution to find the maximum sum subarray using Kadane’s algorithm.
18. Explain the concept of backtracking with an example (e.g., N-Queens problem).
19. Which of the following problems cannot be solved using dynamic programming?
A) Longest common subsequence
B) Matrix chain multiplication
C) Traveling Salesman Problem
D) Binary search
20. Given two strings, write a function to determine if one is a permutation of the other using character frequency counting.
#CompetitiveProgramming #Algorithms #InterviewPrep #CodeForces #LeetCode #ProgrammingContest #DataStructures #AlgorithmDesign
By: @DataScienceQ 🚀
1. Which of the following time complexities represents the most efficient algorithm?
A) O(n²)
B) O(2ⁿ)
C) O(log n)
D) O(n log n)
2. What will be the output of the following code?
def mystery(n):
if n <= 1:
return 1
return n * mystery(n - 1)
print(mystery(5))
3. Write a function to find the longest increasing subsequence in an array using dynamic programming.
4. Explain the difference between BFS and DFS in graph traversal, and when each is preferred.
5. Given a sorted array of integers, which algorithm can be used to find a target value in O(log n) time?
A) Linear search
B) Binary search
C) Bubble sort
D) Merge sort
6. What is the time complexity of the following code snippet?
for i in range(n):
for j in range(i, n):
print(i, j)
7. Write a program to implement Dijkstra's algorithm for finding the shortest path in a weighted graph.
8. What does the term "greedy choice property" refer to in greedy algorithms?
9. Which data structure is most suitable for implementing a priority queue efficiently?
A) Stack
B) Queue
C) Binary heap
D) Linked list
10. What will be the output of this code?
import sys
sys.setrecursionlimit(10000)
def f(n):
if n == 0:
return 0
return n + f(n-1)
print(f(999))
11. Implement a recursive function to compute the nth Fibonacci number with memoization.
12. Describe the concept of divide and conquer with an example.
13. What is the space complexity of quicksort in the worst case?
A) O(1)
B) O(log n)
C) O(n)
D) O(n²)
14. Write a function that checks whether a given string is a palindrome using recursion.
15. In the context of competitive programming, what is the purpose of using bit manipulation?
16. What will be the output of the following code?
s = "abc"
print(s[1:3] + s[0])
17. Design a solution to find the maximum sum subarray using Kadane’s algorithm.
18. Explain the concept of backtracking with an example (e.g., N-Queens problem).
19. Which of the following problems cannot be solved using dynamic programming?
A) Longest common subsequence
B) Matrix chain multiplication
C) Traveling Salesman Problem
D) Binary search
20. Given two strings, write a function to determine if one is a permutation of the other using character frequency counting.
#CompetitiveProgramming #Algorithms #InterviewPrep #CodeForces #LeetCode #ProgrammingContest #DataStructures #AlgorithmDesign
By: @DataScienceQ 🚀
In Python, the itertools module is a powerhouse for creating efficient iterators that handle combinatorial, grouping, and infinite sequence operations—essential for acing coding interviews with elegant solutions! 🌪
By: @DataScienceQ⭐️
#Python #CodingInterview #itertools #DataStructures #Algorithm #Programming #TechJobs #LeetCode #DeveloperTips #CareerGrowth
import itertools
# Infinite iterators - Handle streams with precision
count = itertools.count(start=10, step=2)
print(list(itertools.islice(count, 3))) # Output: [10, 12, 14]
cycle = itertools.cycle('AB')
print(list(itertools.islice(cycle, 4))) # Output: ['A', 'B', 'A', 'B']
repeat = itertools.repeat('Hello', 3)
print(list(repeat)) # Output: ['Hello', 'Hello', 'Hello']
# Combinatorics made easy - Solve permutation puzzles
print(list(itertools.permutations('ABC', 2)))
# Output: [('A','B'), ('A','C'), ('B','A'), ('B','C'), ('C','A'), ('C','B')]
print(list(itertools.combinations('ABC', 2)))
# Output: [('A','B'), ('A','C'), ('B','C')]
print(list(itertools.combinations_with_replacement('AB', 2)))
# Output: [('A','A'), ('A','B'), ('B','B')]
# Cartesian products - Matrix operations simplified
print(list(itertools.product([1,2], ['a','b'])))
# Output: [(1,'a'), (1,'b'), (2,'a'), (2,'b')]
# Practical use: Generate all possible IP octets
octets = [str(i) for i in range(256)]
ips = itertools.product(octets, repeat=4)
print('.'.join(next(ips))) # Output: 0.0.0.0
# Grouping consecutive duplicates - Log analysis superpower
data = 'AAAABBBCCDAA'
groups = [list(g) for k, g in itertools.groupby(data)]
print([k + str(len(g)) for k, g in itertools.groupby(data)])
# Output: ['A4', 'B3', 'C2', 'D1', 'A2']
# Real-world application: Compress sensor data streams
sensor_data = [1,1,1,2,2,3,3,3,3]
compressed = [(k, len(list(g))) for k, g in itertools.groupby(sensor_data)]
print(compressed) # Output: [(1,3), (2,2), (3,4)]
# Chaining multiple iterables - Database query optimization
list1 = [1,2,3]
list2 = ['a','b','c']
chained = itertools.chain(list1, list2)
print(list(chained)) # Output: [1,2,3,'a','b','c']
# Memory-efficient merging of large files
def merge_files(*filenames):
return itertools.chain.from_iterable(open(f) for f in filenames)
# Slicing iterators like lists - Pagination made easy
numbers = itertools.islice(range(100), 5, 15, 2)
print(list(numbers)) # Output: [5,7,9,11,13]
# Interview favorite: Generate Fibonacci with islice
def fib():
a, b = 0, 1
while True:
yield a
a, b = b, a+b
print(list(itertools.islice(fib(), 10))) # Output: [0,1,1,2,3,5,8,13,21,34]
# tee iterator - Process data in parallel pipelines
data = [1,2,3,4]
iter1, iter2 = itertools.tee(data, 2)
print(sum(iter1), max(iter2)) # Output: 10 4
# Warning: Consume original iterator immediately!
original = iter([1,2,3])
t1, t2 = itertools.tee(original)
print(list(t1), list(t2)) # Output: [1,2,3] [1,2,3]
# Interview Gold: Find all subsets (power set)
def powerset(iterable):
s = list(iterable)
return itertools.chain.from_iterable(
itertools.combinations(s, r) for r in range(len(s)+1)
)
print(list(powerset('ABC')))
# Output: [(), ('A',), ('B',), ('C',), ('A','B'), ('A','C'), ('B','C'), ('A','B','C')]
# Interview Gold: Solve "Word Break" problem
def word_break(s, word_dict):
dp = [False] * (len(s)+1)
dp[0] = True
for i in range(1, len(s)+1):
for j in range(i):
if dp[j] and s[j:i] in word_dict:
dp[i] = True
break
return dp[-1]
print(word_break("leetcode", {"leet", "code"})) # Output: True
# Pro Tip: Memory-efficient large data processing
with open('huge_file.txt') as f:
# Process 1000-line chunks without loading entire file
for chunk in iter(lambda: list(itertools.islice(f, 1000)), []):
process(chunk)
By: @DataScienceQ
#Python #CodingInterview #itertools #DataStructures #Algorithm #Programming #TechJobs #LeetCode #DeveloperTips #CareerGrowth
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