Uber OA is a critical hurdle for engineering and data science candidates, designed to evaluate problem-solving skills, algorithmic thinking, anddomain-specific knowledge. Uber OA typically includes coding challenges and analytical problems tailored to Uber’s business needs,such as ride-sharing logistics, route optimization, and real-time data processing.
Introduction to Uber OA
Uber’s OA varies by role but often includes:
- Coding Questions: Focus on data structures, algorithms, and efficiency (e.g., graphs for route planning).
- System Design Elements: High-level design thinking for scalability (common in senior roles).
- Behavioral/Logical Questions: Problem-solving under time pressure; Uber values balancing correctness with practicality.
The key to success? Practice clear, efficient code with careful handling of edge cases.
Uber OA Practice Questions
1. Ride Matching with Dynamic Pricing
Problem:
Uber needs to match drivers to riders based on proximity and dynamic pricing. Given an N x N grid, each cell is a location. Drivers and riders are at specific coordinates. Each rider has a price they’re willing to pay, and each driver has a max_distance they can travel.
Task:
For each rider, find the nearest driver within their max_distance who can reach them at or below their price. Return a list of (rider_index, driver_index) pairs, using the lowest price (if prices tie, choose the closer driver).
Input:
drivers: List of(x, y, max_distance, price_per_km)riders: List of(x, y, max_distance, max_price)
Output:
List of (rider_index, driver_index) or -1 if no match.
Example:
drivers = [(1, 2, 5, 2), (3, 4, 3, 3)]
riders = [(2, 3, 4, 10), (5, 6, 2, 5)]
Output:
[(0, 0), (1, -1)]
Explanation:
Rider 0 at (2,3) is within 4 km of Driver 0 at (1,2) (distance ≈1.414 km). Cost ≈1.414×2 = 2.83 ≤ 10.
Rider 1 at (5,6) is ≈2.828 km from Driver 1; exceeds max_distance.
2. Trip Duration Prediction
Problem:
Predict trip durations based on historical data. Given trips with (start_time, end_time, distance, traffic_condition), estimate the duration of a newtrip.
Task:
Calculate the average duration for trips in distance buckets (0–5 miles, 5–10 miles, >10 miles) and matching trafficconditions.
Input: trips: List of (start_time, end_time, distance, traffic_condition)
new_trip: (distance, traffic_condition)
Output:
Average duration in minutes for the matching bucket, or -1 if no data exists.
Example:trips = [
("2023-10-01 10:00:00", "2023-10-01 10:20:00", 3, "low"),
("2023-10-01 11:30:00", "2023-10-01 12:10:00", 7, "medium"),
("2023-10-02 08:00:00", "2023-10-02 09:30:00", 15, "high"),
]
new_trip = (6, “medium”)
Output: 40
Explanation:
The new trip is in the 5–10 miles / “medium” bucket; the 7 mile example averaged 40 minutes.
3. Driver Scheduling with Availability Windows
Problem:
Drivers set availability windows (start_available, end_available). Given a trip request with trip_start and duration, find all drivers whose availability covers the entire trip.
Task:
Return (count, [driver_ids]), sorted by availability start time.
Input:
drivers: List of (driver_id, start_available, end_available)
trip_start: Integer (e.g., Unix timestamp)
trip_duration: Integer (minutes)
Example:drivers = [
(123, 1000, 1500),
(456, 1200, 1600),
(789, 900, 1100),
]
trip_start = 1100
trip_duration = 200 # ends at 1300
Output: (2, [123, 456])
Stress-Free Exam Support!
Overwhelmed by exams? Programhelp’s professional exam proxy services deliver precise, efficient results—helping youpass with ease. Get in touch today for a customized plan!
