Dynamic Programming Questions at Expedia: What to Expect

Dynamic Programming (DP) is a critical skill for technical interviews at Expedia, especially for roles involving backend systems, optimization, and large-scale data processing. Expedia’s platform handles complex problems like pricing algorithms, route optimization, inventory management, and resource allocation—all areas where DP’s ability to break down overlapping subproblems and cache results leads to efficient, scalable solutions. With 7 out of their 54 total coding questions tagged as Dynamic Programming, mastering this paradigm is a significant differentiator. It signals to interviewers that you can think recursively about a problem, optimize brute-force approaches, and design systems that perform under constraints, which is essential for building the high-performance services Expedia relies on.

What to Expect — Types of Problems

Expedia’s DP questions typically focus on practical, high-frequency patterns rather than obscure puzzles. You can expect:

• Knapsack & Subset Problems: These model resource allocation, such as selecting optimal travel packages or budget-constrained feature sets. Variations include 0/1 Knapsack and Partition Equal Subset Sum.
• String & Sequence DP: Common for tasks involving user inputs, itinerary matching, or data validation. Look for Longest Common Subsequence, Edit Distance, and Palindrome-related problems.
• 1D/2D DP on Arrays: Used for optimization over sequences, like maximizing profit from hotel bookings or minimizing costs in a scheduling system. Problems often involve House Robber, Maximum Subarray, or unique paths on a grid.
• Caching/Memoization of Recursive Solutions: Many problems start as a recursive tree that can be optimized with top-down memoization, testing your ability to identify and eliminate redundant computations.

The problems are designed to assess both your algorithmic thinking and your ability to implement clean, efficient code under interview conditions.

How to Prepare — Study Tips with One Code Example

Start by understanding the core principle: DP is “smart recursion.” It avoids recomputing the same subproblem by storing its result. Follow these steps:

1. Identify the DP property: Does the problem have overlapping subproblems and optimal substructure? Can it be broken down into smaller, similar decisions?
2. Define the state: What parameters uniquely represent a subproblem? This often becomes the index/key in your DP array or memo.
3. Formulate the recurrence relation: Write the decision logic that connects states. This is the core of the solution.
4. Implement: Choose top-down (memoized recursion) for clarity or bottom-up (tabular DP) for optimal space, and handle base cases.

A fundamental pattern is the Fibonacci sequence, which directly demonstrates overlapping subproblems. The naive recursive solution is exponentially slow, but DP optimizes it to linear time.

def fib_memo(n, memo={}):
    if n in memo:
        return memo[n]
    if n <= 1:
        return n
    memo[n] = fib_memo(n-1, memo) + fib_memo(n-2, memo)
    return memo[n]

def fib_tab(n):
    if n <= 1:
        return n
    dp = [0] * (n + 1)
    dp[1] = 1
    for i in range(2, n + 1):
        dp[i] = dp[i-1] + dp[i-2]
    return dp[n]

Recommended Practice Order

Build proficiency systematically:

1. Foundations: Master Fibonacci, Climbing Stairs, and House Robber to internalize 1D DP.
2. Core Patterns: Practice 0/1 Knapsack, Longest Common Subsequence, and Coin Change (unbounded knapsack). These form the basis for many variations.
3. String & Grid DP: Solve Edit Distance, Palindrome Partitioning, and Unique Paths.
4. Expedia-Specific Practice: Focus on problems tagged under Expedia’s company page that involve DP, simulating the actual interview environment.

Consistent practice with a focus on deriving the recurrence relation yourself—not just memorizing solutions—is key.

Practice Dynamic Programming at Expedia