Breadth-First Search Questions at Josh Technology: What to Expect

Breadth-First Search (BFS) is a core algorithmic technique that Josh Technology consistently tests in its technical interviews. With 8 out of their 36 total coding questions dedicated to BFS, it's clear they prioritize candidates who can model problems as graph traversals and implement efficient, level-order solutions. Mastering BFS is not just about solving tree problems; it's about demonstrating you can think in terms of states, transitions, and shortest paths in unweighted graphs—a skill critical for software development roles at the company.

What to Expect — Types of Problems

Josh Technology's BFS questions typically fall into three categories. First, classic grid traversal problems, where you navigate a 2D matrix (like a maze or dungeon) from a start point to a target, often with obstacles. Second, tree level-order operations, including printing levels, finding the minimum depth, or calculating level sums. Third, implicit graph problems, where you must construct a graph of possible states (like transforming a word or solving a puzzle) and find the shortest sequence of moves. Expect constraints that require an optimal shortest-path solution, making BFS the natural choice over Depth-First Search.

How to Prepare — Study Tips with One Code Example

Focus on the BFS template: use a queue, track visited nodes, and process nodes level by level. Practice writing it from memory in your preferred language. Key tips: always mark a node as visited when you enqueue it to avoid duplicates, and handle empty graph cases. For grid problems, pre-define direction vectors (up, down, left, right). The most common pattern is finding the shortest path in an unweighted graph.

Here is the essential BFS template for a grid shortest-path problem, shown in three languages:

from collections import deque

def shortest_path(grid, start, target):
    if not grid:
        return -1
    rows, cols = len(grid), len(grid[0])
    directions = [(1,0), (-1,0), (0,1), (0,-1)]
    queue = deque([(start[0], start[1], 0)])  # (row, col, distance)
    visited = set([(start[0], start[1])])

    while queue:
        r, c, dist = queue.popleft()
        if (r, c) == (target[0], target[1]):
            return dist
        for dr, dc in directions:
            nr, nc = r + dr, c + dc
            if 0 <= nr < rows and 0 <= nc < cols and grid[nr][nc] == 0 and (nr, nc) not in visited:
                visited.add((nr, nc))
                queue.append((nr, nc, dist + 1))
    return -1

Recommended Practice Order

Start with basic tree level-order traversal to internalize the queue mechanism. Move to binary tree problems like finding the minimum depth. Then, tackle grid-based shortest path problems (e.g., "Number of Islands" variations). Finally, practice state-space search problems, such as word ladder or sliding puzzle challenges. This progression builds from simple structures to complex implicit graphs, covering the full scope of Josh Technology's question bank.

Practice Breadth-First Search at Josh Technology