Amazon vs DoorDash: Interview Question Comparison

When preparing for technical interviews at top tech companies, understanding the specific focus and expectations of each can dramatically improve your efficiency. Amazon and DoorDash, while both requiring strong algorithmic skills, present distinct interview landscapes in terms of scale, question difficulty, and topic emphasis. This comparison breaks down their key differences to help you tailor your preparation strategy.

Question Volume and Difficulty

The most striking difference is the sheer volume of documented questions. Amazon's list of 1,938 questions is massive, dwarfing DoorDash's 87 questions. This reflects Amazon's longer interview history, larger number of roles, and the widespread sharing of experiences online.

The difficulty distributions also tell a story:

• Amazon (E530/M1057/H351): The difficulty is weighted towards Medium questions, which make up over half of the total. This aligns with the typical on-site loop, where most questions are designed to be solved in 30-45 minutes with a strong, optimal solution. The significant number of Hard questions often appears in later rounds for more senior positions or specific teams.
• DoorDash (E6/M51/H30): The distribution here is heavily skewed towards Medium and Hard problems relative to its total. Easy questions are minimal. This suggests DoorDash interviews frequently dive directly into complex scenarios, testing your ability to handle nuanced system design or multi-step algorithmic challenges from the outset. The smaller pool means questions may be repeated or have slight variations more often.

Topic Overlap

Both companies heavily test core data structures. Array, String, and Hash Table problems are fundamental for both. This is your essential foundation.

The primary divergence appears in the next layer of focus:

• Amazon's fourth most frequent topic is Dynamic Programming (DP). This is a critical area for Amazon, especially for optimizing problems related to resource allocation, paths, and sequences. Expect to see classic DP patterns.
• DoorDash's fourth most frequent topic is Depth-First Search (DFS). This aligns with problems involving hierarchies, menus, geographical areas, or state exploration—scenarios common in delivery logistics, mapping, and platform management.

Here’s a quick example illustrating a common pattern for each:

# Amazon-style DP: Climbing Stairs (Classic)
def climbStairs(n: int) -> int:
    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]

# DoorDash-style DFS: Clone a N-ary Tree (Hierarchy)
class Node:
    def __init__(self, val=None, children=None):
        self.val = val
        self.children = children if children is not None else []

def cloneTree(root: 'Node') -> 'Node':
    if not root:
        return None
    clone = Node(root.val)
    for child in root.children:
        clone.children.append(cloneTree(child))
    return clone

Which to Prepare for First

Start with the shared foundation. Grind problems on Arrays, Strings, and Hash Tables until you can identify patterns and edge cases quickly. This core is non-negotiable for both.

If your goal is Amazon, you must dedicate significant time to Dynamic Programming. Practice the common patterns (0/1 knapsack, LCS, LIS, etc.) until you can derive the state transition. The vast question pool means you should focus on pattern recognition over memorization.

If your goal is DoorDash, after mastering the core, prioritize Graph traversal (DFS/BFS) and tree problems. Think about real-world applications: mapping routes, managing order states, or navigating menu categories. The smaller question pool makes recent problem lists highly valuable.

Ultimately, your preparation should be company-targeted. Use the massive Amazon list for broad pattern practice, and the focused DoorDash list for deep, scenario-based drilling.

• Practice Amazon questions: /company/amazon
• Practice DoorDash questions: /company/doordash