Razorpay | OA (1st Round) | Senior MLE | Bengaluru
Anonymous User
1486
Dynamic ProgrammingGreedyData ScienceMachine Learning EngineerMachine LearningBengaluruInterviewData Scientist / Data Engineer

Recently I gave Online Assesment at Razorpay in June 2025 and these were the questions which were asked. I have 4 years of experience as a data scientist, the role was Senior Machine Learning Engineer.

Platform: HackerEarth
Mode: Online Coding Test
Round: 1 (Online Assessment)
No. of Questions: 3
Duration: 90 Minutes
Difficulty: Medium to Hard

📌 Question 1: Cache It (LFU Cache Implementation)

Problem Statement:

Implement a cache using the Least Frequently Used (LFU) eviction policy. When the cache reaches its capacity, remove the least frequently used key. If there's a tie, remove the smallest key among those with the least frequency.

Function Signature:

def solve(N: int, Q: int, operations: List[List[int]]) -> List[int]

Parameters:

  • N: Capacity of the cache
  • Q: Number of operations
  • operations: List of operations where:
    • Type 1: 1 key → Get value from cache
    • Type 2: 2 key value → Insert/Update key-value pair

Constraints:

  • 1 ≤ N ≤ 5 * 10^4
  • 1 ≤ Q ≤ 2 * 10^5
  • 1 ≤ key ≤ 2 * 10^5
  • 1 ≤ value ≤ 10^9

Sample Input:

2
5
1 2 -1
2 1 3
2 2 4
2 4 5
1 2 -1

Sample Output:

-1 4

📌 Question 2: Slice Master (Minimum Points Partition)

Problem Statement:

You're given a string S. Split it into parts such that each character appears in at most one part. The score of a part is the square of its length. Minimize the total score across all parts.

Function Signature:

def solve(S: str) -> int

Scoring Rule:

  • For part of length x, score = x^2

Constraints:

  • 1 ≤ len(S) ≤ 500
  • S[i] contains lowercase letters

Sample Input:

eccbbbbdec

Sample Output:

100

📌 Question 3: Shopping Mall (Min Cost with Constraints - Knapsack Variant)

Problem Statement:

You're given N types of chip packets, each with:

  • happiness A[i]
  • weight B[i]
  • cost C[i]

Alice wants at least X happiness and at most Y total weight while minimizing the cost.

Function Signature:

def solve(N: int, A: List[int], B: List[int], C: List[int], X: int, Y: int) -> int

Parameters:

  • A: Happiness values (length N)
  • B: Weights (length N)
  • C: Costs (length N)
  • X: Minimum required happiness
  • Y: Maximum allowed total weight

Constraints:

  • 1 ≤ N ≤ 100
  • 1 ≤ A[i], B[i], C[i], X, Y ≤ 100
  • 1 ≤ C[i] ≤ 10^4

Sample Input:

5
1 2 3 4 5
1 2 3 4 5
5 4 3 2 1
10
10

Sample Output:

5

💬 Final Thoughts:

  • The test had a mix of system design logic (LFU cache), greedy optimization, and classic DP (knapsack).
  • All questions were algorithmically rich and tested core CS fundamentals.
  • Strong understanding of hash maps, priority queues, and dynamic programming is essential for clearing this round.

Other Interviews:

Google

Other Tech Firms

Compensation

Offer / Compensation Discussions

Comments (1)