Breadth-First Search Questions at ServiceNow: What to Expect

Breadth-First Search (BFS) is a fundamental algorithm for exploring graphs and trees level by level. At ServiceNow, a platform built on managing interconnected workflows, data, and services, BFS is a practical tool for solving real-world problems involving network traversal, dependency resolution, and shortest-path finding in unweighted graphs. Its prevalence in their technical interviews—appearing in roughly 10% of their coding questions—signals that interviewers assess a candidate's ability to model problems as graphs and apply systematic traversal.

What to Expect — Types of Problems

ServiceNow BFS questions typically focus on scenarios mirroring platform capabilities. You won't encounter abstract puzzle problems; expect questions grounded in business logic.

1. Shortest Path or Steps in a Network: This is the most common application. Given a network of servers, service dependencies, or user roles, find the minimum number of steps or hops between two points. Example: "Find the minimum number of service upgrades required to resolve a dependency chain."
2. Level-Order Traversal & Tree Analysis: While trees are a type of graph, problems may explicitly involve hierarchical data like organizational charts, category trees, or configuration item relationships. BFS is used to process data level by level.
3. Matrix Traversal (Grid BFS): Problems involve navigating a grid (e.g., a representation of a data center layout or a workflow state table) to find the shortest path, propagate a change, or count reachable cells. Obstacles or specific conditions often define traversable cells.
4. Bidirectional BFS for Efficiency: For advanced questions, especially those searching for a connection between two known points in a large state space, knowledge of Bidirectional BFS can be a differentiator. It significantly reduces the search space.

The key is to recognize the underlying graph: nodes can be servers, states, employees, or grid cells, and edges are the allowed transitions or connections between them.

How to Prepare — Study Tips with One Code Example

Master the standard BFS template using a queue. Your mental checklist should be:

1. Identify the nodes and what defines a valid edge/neighbor.
2. Use a queue to manage the exploration frontier.
3. Use a visited set (or distance map) to avoid cycles and redundant work.
4. Process nodes level by level, often tracking the current level or distance.

The most critical pattern is BFS for shortest path in an unweighted graph. Here is the template applied to a problem of finding the shortest path in a simple graph represented by an adjacency list.

from collections import deque

def bfs_shortest_path(graph, start, target):
    if start == target:
        return 0

    queue = deque([start])
    visited = {start: 0}  # Map node -> distance

    while queue:
        current_node = queue.popleft()
        current_dist = visited[current_node]

        for neighbor in graph.get(current_node, []):
            if neighbor not in visited:
                if neighbor == target:
                    return current_dist + 1
                visited[neighbor] = current_dist + 1
                queue.append(neighbor)
    return -1  # Target not reachable

Recommended Practice Order

Build competency progressively:

1. Master the Core: Start with pure BFS on explicit graphs and simple tree level-order traversal.
2. Apply to Grids: Practice matrix (2D grid) problems where neighbors are up, down, left, right.
3. Solve ServiceNow-like Contexts: Seek out problems tagged "BFS" on platforms like LeetCode, but focus on those with descriptions involving networks, hierarchies, or step minimization.
4. Optimize: Learn to identify when a Bidirectional BFS approach is applicable to cut search time.

Internalize the template so you can adapt it to specific problem constraints—like tracking additional node states or modifying the neighbor generation logic—during the interview.

Practice Breadth-First Search at ServiceNow