Overview
Uber’s online assessment (OA) is a rigorous first hurdle for software engineering candidates, emphasizing algorithms, data structures, andreal-world problem solving. Here are three classic question types you’re likely to encounter:
1. Optimizing City Routes (Shortest-Path Algorithm)
Problem
Given a graph of city intersections and road distances, compute the shortest driving path from a start to a destination.
Example
roads = {("A","B"): 3, ("B","C"): 2, ("A","C"): 5, ("C","D"): 1}
start, dest = "A", "D"
# Shortest path: A → B → C → D (total distance = 6)Approach
Use Dijkstra’s algorithm with a min-heap (priority queue) to efficiently find the minimal-distance path.
Why It Matters at Uber
Real-time route optimization at massive scale—key to dispatching drivers efficiently across a city grid.
2. Order Scheduling (Handling Asynchronous Requests)
Problem
Simulate the sequence in which ride requests are accepted, given each request’s timestamp and priority.
Example
orders = [
{"id": 1, "timestamp": 1, "priority": 3},
{"id": 2, "timestamp": 2, "priority": 1},
{"id": 3, "timestamp": 5, "priority": 2},
]
# Acceptance order → 2 → 3 → 1Approach
1. Sort incoming requests by timestamp.
2. Use a priority queue to pick the highest-priority request available at each step.
Why It Matters at Uber
Uber’s backend must juggle thousands of asynchronous ride and driver events, especially during peak demand.
3. Real-Time Pricing (Calculating Dynamic Fares)
Problem
Compute a fare estimate using distance segments, base rates, and a real-time demand multiplier.
Example
segments = [
{"start":"A","end":"B","distance":10,"base_rate":5},
{"start":"B","end":"C","distance":20,"base_rate":10},
]
demand_multiplier = 1.2
# Fare from A to C = (10*5 + 20*10) * 1.2 = 28Approach
Sum weighted path costs (distance × base_rate) and multiply by demand volatility.
Why It Matters at Uber
Dynamic pricing underpins Uber’s competitive edge—balancing supply, demand, and real-time conditions.
Preparation Tips
- Master graph algorithms (Dijkstra, BFS/DFS, A*).
- Practice priority-queue patterns for scheduling.
- Get comfortable with modeling real-world factors (traffic, surge pricing).
- Emphasize clear, efficient code and handle all edge cases.
Need Extra Support?
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