Amazon vs Uber: Interview Question Comparison

When preparing for technical interviews at major tech companies, understanding the specific focus and patterns of each company's question bank can significantly streamline your study process. Amazon and Uber, while both requiring strong algorithmic problem-solving skills, present distinct profiles in terms of question volume, difficulty distribution, and topical emphasis. A strategic candidate will tailor their preparation to these differences.

Question Volume and Difficulty

The most immediate difference is scale. Amazon's listed question bank is substantially larger, with 1938 total questions compared to Uber's 381. This suggests a broader pool of potential problems, making "grinding" the entire list less practical for Amazon. The difficulty distribution also varies.

Amazon (E530/M1057/H351): The majority of questions are Medium difficulty (1057), with a significant number of Easy (530) and Hard (351) problems. This spread indicates a strong focus on core, medium-complexity algorithmic thinking, with hard questions likely appearing for more senior roles or specific teams.

Uber (E54/M224/H103): The distribution follows a similar pattern but with a slightly higher proportion of Hard questions relative to the total. With 103 Hard questions out of 381 (about 27%), compared to Amazon's 351 out of 1938 (about 18%), Uber's question set may have a reputation for presenting more challenging problems, or it may reflect a more curated, advanced list.

Topic Overlap

Both companies heavily emphasize the same four fundamental data structure categories: Array, String, Hash Table, and Dynamic Programming. This overlap is critical—mastering these topics forms the universal bedrock for both interviews.

• Array & String: Manipulation, searching, sorting, and sliding window techniques are essential.
• Hash Table: Used for efficient lookups, frequency counting, and solving problems involving pairs or duplicates.
• Dynamic Programming: For optimization problems involving sequences, paths, or choices.

The primary difference lies not in which topics, but in how they are applied. Amazon's vast question set may include more problems rooted in real-world e-commerce, logistics, or system design scenarios (even in coding rounds). Uber's problems might more frequently involve location data, rate limiting, or marketplace matching logic. However, the underlying algorithmic patterns remain consistent across both.

For example, a common pattern is finding a subarray with a given sum or property:

# Python: Find number of subarrays with sum k
def subarraySum(nums, k):
    count = 0
    prefix_sum = 0
    sum_map = {0: 1}
    for num in nums:
        prefix_sum += num
        count += sum_map.get(prefix_sum - k, 0)
        sum_map[prefix_sum] = sum_map.get(prefix_sum, 0) + 1
    return count

Which to Prepare for First

Prepare for Amazon first. The reasoning is twofold. First, the core topics (Array, String, Hash Table, DP) are identical. By tackling Amazon's larger and medium-difficulty-focused problem set, you will build a robust foundation that directly applies to Uber's interviews. Second, mastering a broader range of problems from a larger bank inherently prepares you for a more curated, potentially difficult set. If you can solve a wide variety of Amazon's Medium problems, you will be well-positioned for Uber's Hard questions, as they often combine the same fundamental patterns in more complex ways.

Your study plan should be topic-driven, not company-driven initially. Use the company tags to refine your practice later. Solve 150-200 high-frequency problems from Amazon's list, ensuring deep understanding of each solution and pattern. Then, transition to Uber's list, where you will likely find many problems are variations of patterns you've already mastered, allowing you to focus on the unique twists or higher complexity.

For targeted practice, visit the company pages: Amazon and Uber.