Greedy Questions at ServiceNow: What to Expect

Greedy algorithms are a small but critical part of ServiceNow's technical interview repertoire. With 8 out of their 78 total coding questions tagged as Greedy, you have roughly a 1 in 10 chance of encountering one. While this may seem like a low percentage, these questions are often used to assess a candidate's ability to identify optimal local choices that lead to a globally optimal solution—a key skill for designing efficient workflows and automation logic within the ServiceNow platform. Successfully solving a greedy problem demonstrates strong problem decomposition and logical reasoning, which are highly valued for platform development and scripting roles.

What to Expect — Types of Problems

ServiceNow's greedy questions typically fall into predictable categories that align with real-world platform scenarios. You are most likely to encounter problems involving scheduling, resource allocation, or array transformations.

• Interval Scheduling: Classic problems like "meeting rooms" or "non-overlapping intervals" test your ability to maximize the number of tasks or events that can be completed, mirroring priority-based task scheduling in IT workflows.
• Assignment & Partitioning: Problems that require dividing resources or groups efficiently, such as assigning tasks to minimize wait time or splitting an array into fair subsets. This relates to load balancing or ticket assignment logic.
• Array Greedy Transformations: Questions where you make a series of optimal local moves to achieve a final state, like minimizing the cost to make an array increasing or deciding when to buy/sell a resource. This pattern is analogous to automating sequential decisions in a business process.

The constraints are usually manageable, but the core challenge is correctly identifying the greedy property—proving to yourself why making the best immediate choice works for the entire problem.

How to Prepare — Study Tips with One Code Example

Your preparation should focus on pattern recognition, not memorization. Start by understanding the proof or intuition behind classic greedy solutions. Why does sorting by end time work for interval scheduling? Why does processing the most frequent task first work for CPU scheduling? Practice verbalizing this reasoning.

When solving a new problem, ask: "If I make the best possible move right now, will it inevitably lead to the best overall outcome?" If the answer is yes, you likely have a greedy solution. Always test your chosen greedy heuristic against edge cases.

A fundamental pattern is the "Greedy Choice Based on Sorting." You often sort the input data by a specific key (like end time, value, or cost) to make optimal decisions in a single pass.

# Example: Minimum Number of Arrows to Burst Balloons (Find non-overlapping intervals)
def findMinArrowShots(points):
    if not points:
        return 0
    # Sort intervals by their end coordinate
    points.sort(key=lambda x: x[1])
    arrows = 1
    current_end = points[0][1]

    for start, end in points[1:]:
        # If the next balloon starts after the last one ended,
        # we need a new arrow (a new interval).
        if start > current_end:
            arrows += 1
            current_end = end
    return arrows

Recommended Practice Order

Tackle problems in increasing difficulty to build confidence. Begin with foundational problems like "Assign Cookies" or "Lemonade Change" to grasp basic greedy decision-making. Move to core patterns with "Non-overlapping Intervals" and "Merge Intervals." Then, challenge yourself with more complex problems like "Task Scheduler" or "Minimum Time to Make Rope Colorful," which combine greedy logic with other concepts like counting or priority queues. Always time yourself to simulate interview pressure.

Practice Greedy at ServiceNow