How to Crack JPMorgan Coding Interviews in 2026

JPMorgan's coding interviews assess fundamental problem-solving and data structure knowledge. The process typically involves one or more technical rounds focusing on algorithmic questions, often conducted via platforms like HackerRank or in a live coding environment. While not solely focused on extreme algorithmic difficulty, a strong grasp of core concepts is essential to pass.

By the Numbers

The reported data shows a clear emphasis on foundational and practical problem-solving. With 78 questions analyzed, the difficulty breakdown is 32% Easy, 58% Medium, and only 10% Hard. This tells you that JPMorgan's coding interviews are not about solving obscure, complex algorithms under immense pressure. Instead, they prioritize your ability to reliably and cleanly solve common problems. The high percentage of Medium questions is your key insight: you must be proficient in taking a known concept and applying it to a moderately twisted problem. The 10% Hard indicates you should be prepared for a challenge, but your primary focus should be on mastering Mediums and building speed and accuracy on Easies.

Top Topics to Focus On

The top five topics—Array, String, Hash Table, Sorting, and Math—form the bedrock of these interviews. Your preparation should be deeply rooted in these areas.

• Array: This is the most frequent topic. Expect questions on traversal, sliding window, two-pointer techniques, and subarray problems. Practice until manipulating indices and elements is second nature.

  Example: Two Sum (Classic Hash Table/Array Problem) Given an array of integers nums and an integer target, return the indices of the two numbers that add up to the target. You can assume exactly one solution exists.

def two_sum(nums, target):
    # Use a hash map to store number -> index
    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 []  # Should never reach here per problem guarantee

# Example usage
print(two_sum([2, 7, 11, 15], 9))  # Output: [0, 1]

• String: Often paired with Array problems. Focus on anagram checks, palindrome validation, string matching, and common string manipulation functions. Know how to efficiently convert between strings and character arrays.

  Example: Valid Palindrome Given a string s, determine if it is a palindrome, considering only alphanumeric characters and ignoring case.

def is_palindrome(s):
    # Two-pointer approach
    left, right = 0, len(s) - 1
    while left < right:
        # Skip non-alphanumeric characters
        while left < right and not s[left].isalnum():
            left += 1
        while left < right and not s[right].isalnum():
            right -= 1
        # Compare characters (case-insensitive)
        if s[left].lower() != s[right].lower():
            return False
        left += 1
        right -= 1
    return True

# Example usage
print(is_palindrome("A man, a plan, a canal: Panama"))  # Output: True
print(is_palindrome("race a car"))  # Output: False

• Hash Table: Your primary tool for optimizing time complexity. Be ready to use it for frequency counting, memoization, and providing O(1) lookups to solve problems that would otherwise be O(n²).

  Example: First Unique Character in a String Given a string s, find the first non-repeating character and return its index. If it doesn't exist, return -1.

def first_uniq_char(s):
    # Build frequency count
    freq = {}
    for char in s:
        freq[char] = freq.get(char, 0) + 1
    # Find first character with frequency 1
    for i, char in enumerate(s):
        if freq[char] == 1:
            return i
    return -1

# Example usage
print(first_uniq_char("leetcode"))      # Output: 0 ('l' is first unique)
print(first_uniq_char("loveleetcode"))  # Output: 2 ('v' is first unique)

• Sorting: Understand not just how to call a library sort, but the concepts behind common algorithms. Many problems become trivial once the data is sorted; recognizing this pattern is the skill being tested.

  Example: Merge Sort Implementation Understanding sorting algorithms is crucial. Here's a classic divide-and-conquer approach.

def merge_sort(arr):
    if len(arr) <= 1:
        return arr

    # Divide
    mid = len(arr) // 2
    left = merge_sort(arr[:mid])
    right = merge_sort(arr[mid:])

    # Conquer (merge)
    return merge(left, right)

def merge(left, right):
    result = []
    i = j = 0

    while i < len(left) and j < len(right):
        if left[i] <= right[j]:
            result.append(left[i])
            i += 1
        else:
            result.append(right[j])
            j += 1

    # Append remaining elements
    result.extend(left[i:])
    result.extend(right[j:])
    return result

# Example usage
arr = [38, 27, 43, 3, 9, 82, 10]
sorted_arr = merge_sort(arr)
print(sorted_arr)  # Output: [3, 9, 10, 27, 38, 43, 82]

• Math: Questions here are often about number properties, modular arithmetic, or basic combinatorics. They test logical reasoning and your ability to handle potential overflow or edge cases with large numbers.

  Example: Reverse Integer Given a signed 32-bit integer x, return x with its digits reversed. If reversing causes the value to go outside the signed 32-bit integer range, return 0.

def reverse_integer(x):
    INT_MIN, INT_MAX = -2**31, 2**31 - 1
    sign = -1 if x < 0 else 1
    x = abs(x)

    reversed_num = 0
    while x > 0:
        digit = x % 10
        # Check for overflow before multiplying
        if reversed_num > (INT_MAX - digit) // 10:
            return 0
        reversed_num = reversed_num * 10 + digit
        x //= 10

    return sign * reversed_num

# Example usage
print(reverse_integer(123))    # Output: 321
print(reverse_integer(-123))   # Output: -321
print(reverse_integer(120))    # Output: 21
print(reverse_integer(1534236469))  # Output: 0 (overflows)

Preparation Strategy

A targeted 4-6 week plan is effective. This assumes you have a basic familiarity with data structures.

Weeks 1-2: Foundation & Topic Mastery Dedicate each day to one of the top five topics. Start with 2-3 Easy problems to warm up, then solve 4-5 Medium problems. Do not just solve for the answer. For each problem, write clean code, analyze time/space complexity aloud, and consider edge cases. Use a hash table day to revisit array and string problems, looking for optimization opportunities.

Example Daily Schedule for Array Week:

• Morning: Review two-pointer technique with problems like "Two Sum II" and "Container With Most Water"
• Afternoon: Practice sliding window with "Maximum Subarray" and "Minimum Size Subarray Sum"
• Evening: Solve subarray problems like "Product of Array Except Self" and "Subarray Sum Equals K"

Weeks 3-4: Pattern Recognition & Speed Shift to mixed-topic practice. Use the company's question list on CodeJeet. Aim to solve 3-4 Medium problems daily under timed conditions (30-45 minutes each). The goal is to correctly identify the underlying pattern (e.g., "this is a sliding window problem using a hash map") within the first few minutes. Begin incorporating a few Hard problems from the top topics to stretch your thinking.

Pattern Recognition Exercise: Given a problem, try to categorize it within 2 minutes:

1. Is it a search problem? (Binary Search)
2. Does it involve finding pairs or triplets? (Two Pointers, Hash Table)
3. Is it about optimal substructure? (Dynamic Programming)
4. Does it involve relationships between elements? (Graph, Union-Find)
5. Can sorting simplify the problem? (Sorting-based solution)

Weeks 5-6: Mock Interviews & Gaps In the final stretch, conduct at least 2-3 mock interviews per week, simulating the actual environment—no IDE, just a whiteboard or plain text editor. Verbally explain your process. Use your performance to identify weak spots and do focused review. Re-solve previously missed problems. Ensure you can derive and code standard sorting algorithms like QuickSort or MergeSort from scratch.

Mock Interview Framework:

1. Clarify Requirements (2 minutes): Ask about input constraints, edge cases, and output format
2. Discuss Approaches (5 minutes): Explain brute force, then optimized solution with complexity analysis
3. Code Implementation (15 minutes): Write clean, commented code while explaining your logic
4. Test & Debug (5 minutes): Walk through test cases including edge cases
5. Discuss Optimizations (3 minutes): Mention potential improvements or alternative approaches

Key Tips

1. Communicate Relentlessly. JPMorgan values clarity. Before you write code, state the brute-force approach and its complexity. Then explain your optimized plan. Talk through your logic as you code. This turns the interview into a collaboration.

   Example Communication Script: "For this problem of finding two numbers that sum to a target, the brute force approach would be to check every pair, which would be O(n²) time and O(1) space. We can optimize this using a hash table to store numbers we've seen, allowing us to find the complement in O(1) time, giving us O(n) time and O(n) space complexity."

2. Prioritize Correctness Over Cleverness. A bug-free, well-explained solution to a Medium problem is better than a half-finished, overly complex one. Use the extra time from solving Easies quickly to carefully reason through your Medium solutions.

3. Test Your Own Code. After writing your solution, walk through a small test case step-by-step. Then consider edge cases: empty input, single element, large values, duplicates. This demonstrates professional habits.

   Common Edge Cases to Consider:

   • Empty array/string/null input
   • Single element
   • All identical elements
   • Already sorted/reverse sorted data
   • Negative numbers
   • Integer overflow
   • Duplicate values
   • Case sensitivity in strings

4. Know Your Resume. Be prepared to discuss any past project or experience listed on your resume in detail, including technical decisions and challenges. The coding interview is part of a broader assessment.

   Technical Discussion Points:

   • Why you chose a particular database or framework
   • How you handled scalability issues
   • What trade-offs you made in system design
   • How you debugged a particularly challenging bug
   • What you learned from a failed implementation

Additional Important Patterns

Beyond the top five topics, be familiar with these additional patterns that frequently appear:

Dynamic Programming: While not in the top five, DP questions do appear. Focus on classic problems like Fibonacci, Coin Change, and Longest Common Subsequence.

def fibonacci(n, memo={}):
    # Memoized Fibonacci - classic DP example
    if n in memo:
        return memo[n]
    if n <= 1:
        return n
    memo[n] = fibonacci(n-1, memo) + fibonacci(n-2, memo)
    return memo[n]

# Example usage
print(fibonacci(10))  # Output: 55

Tree Traversal: Understand both iterative and recursive approaches to tree problems, particularly Binary Search Trees.

Graph Algorithms: Be comfortable with BFS and DFS for traversal problems, especially when they involve grids or networks.

Success in JPMorgan's coding interviews comes from consistent, focused practice on the core topics they care about most. Build a strong foundation, practice articulating your thoughts, and you'll be well-positioned to solve the problems they present.

Browse all JPMorgan questions on CodeJeet