Databricks·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026Remote

Summary

Databricks SWE interview with a grid traversal problem that had a twist: you're not just finding a path, you're optimizing across two competing objectives at once. Felt like a pretty classic setup until the trade-off framing came in and I had to actually justify my algorithmic choice out loud.

Questions Asked (1)

Given a grid with a start and destination cell, find a path that optimizes a combined objective involving both the number of steps taken and a cost associated with cells or edges. You need to define the trade-off yourself (e.g. minimize cost within a step budget, or minimize a weighted combination), then pick and justify the right algorithm: BFS, 0-1 BFS, or Dijkstra.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The part that tripped me up was that they wanted me to define the trade-off before coding anything.

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AI HintsAI Generated

Suggested Approach

First, clarify the objective by explicitly defining the trade-off between steps and cost, such as minimizing cost within a step budget or minimizing a weighted sum. Then, model the problem as a graph where each cell is a node and edges represent moves with associated costs and step increments. Finally, select the appropriate algorithm based on the cost structure and justify why it fits the chosen objective.

Pro tip: Demonstrate awareness that the choice of algorithm depends on the cost structure: if all edge costs are 0 or 1, 0-1 BFS is optimal; if costs are non-negative but arbitrary, Dijkstra is needed; if steps are the only metric, BFS suffices. Also, mention that for weighted combinations, you might need to transform the problem or use multi-criteria optimization techniques.

1. Clarify the objective

Define the exact trade-off between steps and cost, such as minimizing cost subject to a step limit, or minimizing a weighted sum. State any assumptions about cost values (e.g., non-negative, integer).

2. Model as a graph

Represent the grid as a graph where each cell is a node, and edges connect adjacent cells. Assign each edge a cost and a step count (usually 1 per move).

3. Analyze cost structure

Determine the range and nature of edge costs: are they all 0/1, non-negative integers, or arbitrary? This dictates the algorithm choice.

4. Select and justify algorithm

Choose BFS if steps are the only metric; 0-1 BFS if costs are 0/1; Dijkstra if costs are non-negative and arbitrary. Explain why the chosen algorithm optimally solves the defined objective.

5. Discuss extensions and trade-offs

Mention potential complications like negative costs (requiring Bellman-Ford) or multi-objective optimization (e.g., Pareto optimality) and how you would handle them.

Key Points to Mention

BFS is optimal for unweighted graphs (minimizing steps).
0-1 BFS uses a deque to handle 0/1 weights in O(V+E) time.
Dijkstra's algorithm works for non-negative weights and can incorporate both steps and costs if combined into a single weight.
For a step budget constraint, you might need to track both cost and steps, potentially using a modified Dijkstra or dynamic programming.
Weighted combination: if the objective is a linear combination, you can assign each edge a weight = α * cost + β * steps and run Dijkstra.
Multi-criteria optimization may require Pareto frontier or Lagrangian relaxation if the trade-off is not fixed.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.