Dynamic Programming Questions at Agoda: What to Expect

Dynamic Programming (DP) is a core algorithmic technique for optimizing solutions to complex problems by breaking them down into simpler overlapping subproblems. At Agoda, a global online travel platform, DP is highly relevant for backend systems that handle pricing, routing, availability, and resource allocation—all areas where efficient, optimal computation is critical. With 10 out of their 46 total coding questions focused on DP, mastering this paradigm is essential for passing their technical interviews. It demonstrates your ability to think about optimization, state management, and efficient computation at scale.

What to Expect — Types of Problems

Agoda's DP questions typically fall into common categories seen in technical interviews, but often with a practical twist. You can expect:

• Classic 1D/2D DP: Problems like the "House Robber" series, "Coin Change," or "Longest Increasing Subsequence" test your understanding of state definition and transition.
• String/Sequence DP: Problems involving edit distance, longest common subsequence, or palindrome partitioning are common, as they model real-world tasks in data processing and comparison.
• Knapsack-style Problems: These assess your ability to handle optimization with constraints, analogous to resource allocation or feature selection scenarios.
• DP on Grids or Trees: Problems like unique paths, minimum path sum, or tree DP may appear, reflecting challenges in navigation or hierarchical data processing. The problems are designed to evaluate not just your ability to memorize solutions, but to derive the DP state and transitions logically under interview pressure.

How to Prepare — Study Tips with One Code Example

Start by solidifying the core concept: DP is used when a problem has optimal substructure (an optimal solution can be constructed from optimal solutions of its subproblems) and overlapping subproblems. Use a systematic approach:

1. Identify the State: What parameters define a subproblem? (e.g., dp[i] or dp[i][j]).
2. Define the Recurrence Relation: How does the solution to a larger state depend on smaller states?
3. Set Base Cases: What are the smallest, trivial subproblems?
4. Choose an Implementation Order: Top-down (memoized recursion) or bottom-up (iterative tabulation).

A fundamental pattern is the "Fibonacci-style" sequence, which is the foundation for many 1D DP problems. Here is a bottom-up tabulation example:

def fib(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]  # Recurrence relation
    return dp[n]

Practice deriving the state and recurrence for each new problem before looking at solutions. Draw out the state table to visualize the dependencies.

Recommended Practice Order

Do not jump into hard problems immediately. Build competence progressively:

1. Foundation: Start with the Fibonacci sequence and Climbing Stairs to understand 1D state.
2. Classic 1D: Move to House Robber, Coin Change, and Longest Increasing Subsequence.
3. 2D & Strings: Practice Longest Common Subsequence and Edit Distance.
4. Knapsack: Solve 0/1 Knapsack and Partition Equal Subset Sum.
5. Advanced Patterns: Finally, tackle problems like Unique Paths II, DP on trees, or more complex state definitions. For each category, solve at least 2-3 problems to internalize the pattern.

Practice Dynamic Programming at Agoda