Greedy Questions at Agoda: What to Expect

Greedy algorithms are a practical subset of problem-solving that Agoda's interviewers use to assess a candidate's ability to make optimal local choices. With 5 out of their 46 total coding questions tagged as Greedy, it's a pattern you cannot ignore. These questions test not just if you can implement a solution, but if you can recognize the scenario where a simple, step-by-step optimal choice leads to a globally optimal result. For a company optimizing travel bookings, pricing, and resource allocation, this mindset is directly applicable to real-world engineering decisions.

What to Expect — Types of Problems

Agoda's Greedy questions typically fall into a few predictable categories. You are most likely to encounter interval-based problems, such as meeting room scheduling or merging intervals, which are common in system design for handling bookings or events. Assignment problems are also frequent, like task scheduling or assigning resources with the goal of maximizing throughput or minimizing cost. A third category involves array manipulation for optimal outcome, such as deciding jump games, partitioning labels, or maximizing profit from a single transaction. The key is that the problem will have a property—often "greedy choice" or "optimal substructure"—where what's best right now remains best in the final solution.

How to Prepare — Study Tips with Code Example

To prepare, first solidify your understanding of when a greedy approach is valid. A rule of thumb: if you can solve the problem by repeatedly taking the "best" available item without needing to reconsider later, it's likely greedy. Practice by sorting data as a first step—many greedy solutions begin with sorting an array of intervals, tasks, or costs. Then, walk through the sorted data with a single pass, making decisions and tracking a key variable (like a count, end time, or total).

A classic pattern is the "Merge Intervals" problem. The greedy strategy is to sort intervals by their start time, then iterate through them, merging any that overlap.

def merge(intervals):
    if not intervals:
        return []
    # Sort by start time
    intervals.sort(key=lambda x: x[0])
    merged = [intervals[0]]
    for current in intervals[1:]:
        last = merged[-1]
        # If intervals overlap, merge by updating the end time
        if current[0] <= last[1]:
            last[1] = max(last[1], current[1])
        else:
            merged.append(current)
    return merged

Recommended Practice Order

Start with foundational problems to build intuition. First, practice "Maximum Subarray" (Kadane's Algorithm) to understand making optimal local decisions for a global sum. Next, tackle "Merge Intervals" as shown above, a pattern directly applicable to booking systems. Then, move to "Task Scheduler" to handle assignment and scheduling logic. Follow this with "Jump Game" to practice greedy array traversal. Finally, attempt more complex problems like "Gas Station" which combines greedy reasoning with a circular array. This progression builds from basic choice-making to integrated problem-solving.

Practice Greedy at Agoda