Uber vs Expedia: Interview Question Comparison

When preparing for technical interviews, understanding the specific focus and expectations of each company is crucial for efficient study. Uber and Expedia, while both major tech employers, present distinctly different interview landscapes in terms of scale, difficulty, and topic emphasis. A strategic candidate will tailor their preparation accordingly.

Question Volume and Difficulty

The sheer volume of reported questions is the most striking difference. Uber's list of 381 questions is vast, indicating a highly dynamic and frequently refreshed interview process. The difficulty distribution (E54/M224/H103) shows a clear emphasis on Medium and Hard problems, with Hard questions making up a significant portion. This suggests Uber interviews are designed to rigorously test advanced problem-solving and algorithmic optimization under pressure.

In contrast, Expedia's list contains 54 questions, a more manageable and stable set. The difficulty distribution (E13/M35/H6) is heavily skewed toward Easy and Medium problems, with very few Hard questions reported. This indicates Expedia's technical screen likely focuses on core competency, clean code, and practical problem-solving rather than extreme algorithmic complexity.

Key Takeaway: Uber requires deep, broad, and rigorous practice. Expedia requires solid fundamentals and proficiency with common patterns.

Topic Overlap

Both companies prominently feature Array, String, and Hash Table questions. This is standard for software engineering roles, as these data structures form the backbone of most solutions.

• Uber's additional focus on Dynamic Programming is notable and aligns with its difficulty profile. DP questions (e.g., knapsack variants, unique paths, edit distance) are classic Hard/Medium challenges that test optimal substructure thinking.

# Example DP pattern (Uber-relevant): Fibonacci / Min Cost Climbing Stairs
def minCostClimbingStairs(cost):
    n = len(cost)
    dp = [0] * (n + 1)
    for i in range(2, n + 1):
        dp[i] = min(dp[i-1] + cost[i-1], dp[i-2] + cost[i-2])
    return dp[n]

• Expedia's unique listed focus is Greedy Algorithms. These problems (e.g., activity selection, coin change [greedy version where applicable], task scheduling) test the ability to find efficient, step-by-step optimal solutions. This often pairs with array manipulation.

# Example Greedy pattern (Expedia-relevant): Maximum number of meetings
def max_meetings(start, end):
    meetings = sorted(zip(end, start))  # Sort by end time
    count, last_end = 0, 0
    for e, s in meetings:
        if s >= last_end:
            count += 1
            last_end = e
    return count

Key Takeaway: Master arrays, strings, and hash maps for both. Then, prioritize DP for Uber and Greedy patterns for Expedia.

Which to Prepare for First

Prepare for Expedia first if you are early in your interview preparation cycle or prioritizing confidence-building. The smaller question bank and lower difficulty ceiling allow you to achieve coverage and proficiency more quickly. Solidifying fundamentals here will build a strong foundation.

Prepare for Uber first if you are already comfortable with core data structures and are aiming for top-tier tech roles. The extensive preparation required will inherently cover the difficulty level needed for Expedia. Mastering Uber's problem set means you will be over-prepared for Expedia's technical screen, but the reverse is not true.

Ultimately, your choice should align with your target role and current skill level. Use the focused practice for each company's emphasis to maximize your efficiency.

For detailed question lists and patterns, visit the Uber and Expedia question pages: Uber Interview Questions | Expedia Interview Questions