Array Questions at JPMorgan: What to Expect

Array questions make up 46 of the 78 total coding problems in JPMorgan's interview question bank. This 59% concentration means your preparation must be array-centric. Arrays are fundamental because they model real-world financial data sequences—daily stock prices, transaction logs, time-series data, and portfolio holdings. Mastery here demonstrates your ability to manipulate core data structures efficiently, a non-negotiable skill for developing high-frequency trading systems, risk analysis tools, and data processing pipelines.

What to Expect — Types of Problems

JPMorgan's array problems test specific, applied skills beyond basic traversal. Expect these categories:

• Two-Pointer & Sliding Window: These are paramount. Used for finding pairs, subarrays with a target sum (common in analyzing profit/loss windows), or removing duplicates from sorted data. Questions often involve optimizing a brute-force O(n²) solution to O(n).
• Prefix Sum & Hashing: Frequently tested for problems asking for subarray sums equaling a target. This pattern is critical for tasks like finding periods of specific cumulative returns or matching transaction totals.
• In-Place Array Manipulation: Tasks such as rotating arrays, moving zeros, or rearranging elements (e.g., bringing all negatives to one side). These test your understanding of memory efficiency and careful index management.
• Simulation & Matrix Traversal: While less frequent than 1D arrays, you may encounter 2D array (matrix) problems involving spiral order traversal or rotation, simulating the processing of grid-based data.

The problems are practical. You'll be asked to find a contiguous subarray with the largest sum (Kadane's Algorithm for max profit), or to merge and sort multiple lists of financial data, not to implement abstract computer science concepts.

How to Prepare — Study Tips with One Code Example

Focus on pattern recognition, not memorization. Learn the underlying technique for each category. For example, Sliding Window is essential for subarray problems. Here is a template for finding the length of the longest subarray with a sum less than or equal to a target k.

def longest_subarray_sum_at_most_k(nums, k):
    left = 0
    current_sum = 0
    max_length = 0

    for right in range(len(nums)):
        current_sum += nums[right]

        # Shrink window from left if sum exceeds k
        while current_sum > k:
            current_sum -= nums[left]
            left += 1

        # Update max length for valid window
        max_length = max(max_length, right - left + 1)

    return max_length

Study Tip: Practice this pattern until you can code it from scratch. Then, apply the same sliding window logic to related problems, like finding the smallest subarray with a sum >= target.

Recommended Practice Order

1. Foundation: Start with basic traversal, insertion, deletion, and binary search on sorted arrays.
2. Core Patterns: Dedicate most of your time to Two-Pointer and Sliding Window problems. Follow this with Prefix Sum and Hashing patterns.
3. In-Place Operations: Practice rotating arrays, moving elements (like zeros), and Dutch National Flag-type rearrangements.
4. Advanced Application: Tackle problems combining patterns, like using a hash map with a sliding window, or solving 2D array traversal problems.
5. JPMorgan-Specific: Finally, work through all 46 array problems in JPMorgan's tagged list to familiarize yourself with their exact phrasing and difficulty.

Practice Array at JPMorgan