Google vs Amazon: Interview Question Comparison

When preparing for technical interviews at top tech companies, understanding the specific focus areas and question patterns can significantly increase your chances of success. Google and Amazon are two of the most sought-after employers, and their interview processes are rigorous but distinct. While both assess core algorithmic problem-solving, a closer look at their question banks reveals differences in volume, difficulty distribution, and topical emphasis that can inform your study strategy.

Question Volume and Difficulty

The total number of cataloged questions and their breakdown by difficulty provides the first layer of insight.

Google has a larger overall question bank with 2,217 questions, compared to Amazon's 1,938. The difficulty distribution is also slightly more challenging at the higher end. Google's breakdown is approximately 588 Easy (27%), 1153 Medium (52%), and 476 Hard (21%) questions. Amazon's breakdown is roughly 530 Easy (27%), 1057 Medium (55%), and 351 Hard (18%) questions.

This data suggests that while both companies heavily emphasize Medium-difficulty problems, Google places a marginally greater emphasis on Hard questions. You should be prepared for complex, multi-step problems at Google, often requiring optimization beyond the initial brute-force solution. Amazon's interviews are also demanding, but the slightly lower proportion of Hard questions might indicate a stronger focus on practical implementation and clarity within the Medium range.

Topic Overlap

The core technical areas tested are remarkably similar, highlighting the universal fundamentals of software engineering interviews. Both companies' most frequent topics are Array, String, Hash Table, and Dynamic Programming.

This high overlap means a strong foundation in these areas is non-negotiable for either company.

• Arrays & Strings: Expect questions on two-pointer techniques, sliding windows, and in-place manipulations.
• Hash Tables: Crucial for optimizing lookups and solving problems related to frequency counting, duplicates, and complements (like the classic Two Sum).
• Dynamic Programming: A key differentiator for senior levels; you must be comfortable with identifying optimal substructure and overlapping subproblems.

# Example: A common Two Sum problem (Hash Table)
def two_sum(nums, target):
    seen = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in seen:
            return [seen[complement], i]
        seen[num] = i
    return []

# Example: A simple DP problem (Climbing Stairs)
def climb_stairs(n):
    if n <= 2:
        return n
    dp = [0] * (n + 1)
    dp[1], dp[2] = 1, 2
    for i in range(3, n + 1):
        dp[i] = dp[i-1] + dp[i-2]
    return dp[n]

The main difference often lies not in the topics themselves, but in the context. Amazon is more likely to wrap a core algorithm in a scenario related to scalability, customer data, or operational efficiency (e.g., merging orders, optimizing delivery routes). Google questions may lean more towards abstract algorithmic elegance, systems design implications, or novel applications of data structures.

Which to Prepare for First

Given the substantial overlap in core topics, you should prepare for both simultaneously by mastering the fundamentals. Start with the shared pillars: Arrays, Strings, Hash Tables, and Dynamic Programming. A robust understanding here will serve you for either interview.

If you must prioritize, use this guideline: If your goal is to pass the most technically rigorous bar and you are strong at abstract algorithm design, start by diving deep into Google's question bank, as its greater volume and higher proportion of Hard questions will push your limits. If your goal is to align problem-solving with large-scale, practical business applications, begin with Amazon's problems and practice articulating the real-world trade-offs of your solutions.

Ultimately, the best preparation is holistic. Solidify your core skills, then practice contextualizing them—whether for algorithmic depth (Google) or scalable, practical implementation (Amazon).

For targeted practice, visit the CodeJeet question banks: Google Interview Questions | Amazon Interview Questions