How to Crack PhonePe Coding Interviews in 2026

PhonePe's coding interviews are designed to assess strong problem-solving skills and the ability to write clean, efficient code under pressure. The process typically involves multiple rounds focusing on data structures, algorithms, and system design, with a heavy emphasis on practical, implementable solutions. Success requires targeted preparation aligned with their specific technical focus.

By the Numbers

The reported data shows a clear emphasis on challenging problems. With only 3% of questions categorized as Easy, 62% as Medium, and 35% as Hard, PhonePe's interviews are not for the faint of heart. This breakdown signals that interviewers expect candidates to comfortably handle complex problem-solving. You must be prepared to not only solve Medium-difficulty problems consistently but also to grapple with a significant number of Hard problems, often involving multi-step logic or optimization. The low number of Easy questions means there is little warm-up; you are expected to be technically sharp from the start.

Top Topics to Focus On

Mastering the following five areas is non-negotiable, as they form the core of PhonePe's technical assessment.

Array: This is the most fundamental data structure and the bedrock of countless problems. Expect questions on subarrays, rotations, and in-place manipulations. Your ability to traverse and manipulate arrays efficiently is a basic expectation. Common patterns include the sliding window technique for subarray problems, two-pointer approaches for sorted arrays or in-place operations, and prefix sum arrays for range queries. For example, a classic problem is finding the maximum sum of a contiguous subarray (Kadane's Algorithm) or rotating an array in-place.

# Python: Kadane's Algorithm for Maximum Subarray Sum
def max_subarray_sum(nums):
    max_current = max_global = nums[0]
    for i in range(1, len(nums)):
        max_current = max(nums[i], max_current + nums[i])
        if max_current > max_global:
            max_global = max_current
    return max_global

# Example: In-place array rotation using reversal
def rotate_array(nums, k):
    n = len(nums)
    k %= n
    def reverse(arr, start, end):
        while start < end:
            arr[start], arr[end] = arr[end], arr[start]
            start += 1
            end -= 1
    reverse(nums, 0, n - 1)
    reverse(nums, 0, k - 1)
    reverse(nums, k, n - 1)
    return nums

Dynamic Programming: The high percentage of Hard questions is often linked to DP. You must be proficient in identifying overlapping subproblems and optimal substructure, particularly in classical problems involving sequences, partitioning, and optimization. Key patterns include 0/1 Knapsack for resource allocation, Longest Common Subsequence (LCS) for sequence comparison, and Longest Increasing Subsequence (LIS). A solid approach is to first define the state (e.g., dp[i][j]), then the recurrence relation, followed by base cases and the order of computation (often tabulation or memoization).

# Python: 0/1 Knapsack Problem (Tabulation)
def knapsack(weights, values, capacity):
    n = len(weights)
    dp = [[0] * (capacity + 1) for _ in range(n + 1)]
    for i in range(1, n + 1):
        for w in range(1, capacity + 1):
            if weights[i-1] <= w:
                dp[i][w] = max(values[i-1] + dp[i-1][w - weights[i-1]], dp[i-1][w])
            else:
                dp[i][w] = dp[i-1][w]
    return dp[n][capacity]

# Python: Longest Common Subsequence (Memoization)
def lcs_memo(text1, text2):
    from functools import lru_cache
    @lru_cache(None)
    def dfs(i, j):
        if i == len(text1) or j == len(text2):
            return 0
        if text1[i] == text2[j]:
            return 1 + dfs(i + 1, j + 1)
        return max(dfs(i + 1, j), dfs(i, j + 1))
    return dfs(0, 0)

Sorting: It's rarely just about calling sort(). Focus on how sorting enables other algorithms (like two-pointer techniques) and be ready to implement custom comparators or even specific sorting algorithms like quicksort or mergesort if required. Understanding the time and space complexity trade-offs (e.g., quicksort O(n log n) average but O(n²) worst-case, mergesort's stable O(n log n) but O(n) space) is crucial. Sorting is often a pre-processing step to simplify problems, such as finding pairs with a given sum or merging intervals.

# Python: Custom sorting with a comparator (sort by frequency then value)
from collections import Counter
def sort_by_frequency(arr):
    freq = Counter(arr)
    # Sort by descending frequency, then ascending value
    return sorted(arr, key=lambda x: (-freq[x], x))

# Python: Quicksort implementation (in-place)
def quicksort(arr, low, high):
    if low < high:
        pi = partition(arr, low, high)
        quicksort(arr, low, pi - 1)
        quicksort(arr, pi + 1, high)
def partition(arr, low, high):
    pivot = arr[high]
    i = low - 1
    for j in range(low, high):
        if arr[j] <= pivot:
            i += 1
            arr[i], arr[j] = arr[j], arr[i]
    arr[i + 1], arr[high] = arr[high], arr[i + 1]
    return i + 1

Hash Table: This is your primary tool for achieving O(1) lookups and solving problems involving frequency counting, duplicate detection, and mapping relationships. Master its use to optimize solutions that would otherwise be O(n²). Beyond simple lookups, hash tables (or dictionaries/maps) are essential for implementing caches (like LRU Cache), grouping elements (like anagrams), and maintaining state across iterations. For instance, the two-sum problem is a classic example of using a hash map to store complements.

# Python: Two Sum using Hash Map
def two_sum(nums, target):
    num_map = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in num_map:
            return [num_map[complement], i]
        num_map[num] = i
    return []

# Python: Grouping Anagrams using Hash Map
from collections import defaultdict
def group_anagrams(strs):
    anagram_map = defaultdict(list)
    for s in strs:
        key = ''.join(sorted(s))  # Sorted string as key
        anagram_map[key].append(s)
    return list(anagram_map.values())

String: String manipulation questions often combine with other topics like DP or hash tables. Be thoroughly comfortable with operations like substring search, palindrome checks, anagrams, and string transformations. Key algorithms include the Knuth-Morris-Pratt (KMP) algorithm for efficient substring search, and dynamic programming approaches for longest palindromic substring. Understanding how to efficiently reverse, split, concatenate, and compare strings (considering encoding if applicable) is fundamental.

# Python: Check if a string is a palindrome
def is_palindrome(s):
    left, right = 0, len(s) - 1
    while left < right:
        if s[left] != s[right]:
            return False
        left += 1
        right -= 1
    return True

# Python: Longest Palindromic Substring using DP
def longest_palindrome(s):
    n = len(s)
    if n < 2:
        return s
    dp = [[False] * n for _ in range(n)]
    start, max_len = 0, 1
    # All substrings of length 1 are palindromes
    for i in range(n):
        dp[i][i] = True
    # Check substrings of length 2
    for i in range(n - 1):
        if s[i] == s[i + 1]:
            dp[i][i + 1] = True
            start = i
            max_len = 2
    # Check lengths greater than 2
    for length in range(3, n + 1):
        for i in range(n - length + 1):
            j = i + length - 1
            if s[i] == s[j] and dp[i + 1][j - 1]:
                dp[i][j] = True
                start = i
                max_len = length
    return s[start:start + max_len]

Preparation Strategy

A focused 4-6 week plan is essential. Assume you have a foundational knowledge of data structures.

Weeks 1-2: Core Topic Deep Dive. Dedicate each day to one of the top five topics. For each, solve 15-20 curated LeetCode problems, prioritizing Medium difficulty. For example, for Dynamic Programming, start with foundational patterns (knapsack, LCS, LIS) before moving to more complex variants. The goal is not to memorize but to internalize patterns. For Arrays, practice sliding window and two-pointer techniques. For Hash Tables, solve problems involving frequency maps and caching. For Strings, tackle palindrome and substring problems. For Sorting, implement custom comparators and understand algorithm trade-offs.

Weeks 3-4: Problem Intensity and Mock Interviews. Shift to solving PhonePe-tagged problems directly. Mix 2-3 problems daily, ensuring at least one is Hard. Begin timed practice sessions of 45-60 minutes to simulate interview pressure. In the fourth week, start weekly mock interviews with a peer or mentor, focusing on communicating your thought process clearly while coding. Practice writing code on a whiteboard or in a plain text editor without syntax highlighting. Record yourself explaining your approach to improve clarity.

Weeks 5-6: Gap Analysis and Revision. Identify your weak spots from the previous weeks—was it graph problems that occasionally appeared, or a specific DP pattern? Systematically review those concepts. Re-solve previously challenging problems without help. Dedicate time to reviewing system design fundamentals, as this is a likely subsequent round. Create summary sheets for each core topic with key algorithms, their complexities, and common variations. Conduct final mock interviews focusing on weak areas.

Key Tips

1. Optimize Relentlessly. For Medium problems, a brute-force solution is rarely enough. Interviewers will push for the most optimal approach. Always analyze time and space complexity and be prepared to discuss trade-offs. For example, if you propose an O(n²) solution, immediately think about whether sorting, hashing, or dynamic programming can reduce it to O(n log n) or O(n).

2. Communicate Before You Code. Verbally walk through your initial thoughts, a brute-force idea, and then your optimized approach. This demonstrates structured thinking and allows the interviewer to guide you if you're heading in the right direction. For instance, start with: "A naive approach would be to check all pairs, which is O(n²). We can optimize by using a hash map to store seen elements, reducing it to O(n) time with O(n) space."

3. Write Production-Ready Code. Use meaningful variable names, include clear comments for complex logic, and handle edge cases explicitly. Sloppy code, even if the algorithm is correct, will count against you. Always check for null inputs, empty arrays, single-element cases, and large inputs that could cause overflow. Write helper functions when logic becomes nested.

4. Practice on a Whiteboard or Plain Text Editor. Do not rely solely on LeetCode's auto-complete and syntax highlighting. Get comfortable writing syntactically correct code in a minimal environment, as this mirrors the interview setting. Practice writing code by hand or in a simple text editor like Notepad to build muscle memory for syntax and structure.

Targeted, consistent practice on the right topics is the most reliable path to success. Start with the core areas, build up to Hard problems, and simulate the real environment as closely as possible.

Browse all PhonePe questions on CodeJeet