Google Behavioral + Coding Interview Prep

Google Interview Structure

Typical onsite (L4/L5):

• 2x Coding rounds (45 min each)
• 1x System Design (45-60 min, L5+)
• 1x Behavioral / Googleyness (45 min)
• 1x General cognitive ability (via coding)

What "Googleyness" Means

Google evaluates candidates on:

1. Comfort with ambiguity — Can you make progress with vague requirements?
2. Emergent leadership — Do you naturally step up without a title?
3. Collaboration — Do you make the team better?
4. Fun / passion — Are you excited about hard problems?

Questions to expect:

• "Tell me about a time you had to convince people to adopt your approach."
• "Describe a situation where the requirements changed mid-project."
• "What would you work on if you joined Google?"
• "Tell me about a project you're proud of that others didn't value."

Coding Expectations at Google

Google expects you to:

1. Clarify before coding — always ask about constraints
2. Discuss multiple approaches — show breadth of knowledge
3. Write clean modular code — functions, clear names
4. Derive complexity proactively — don't wait to be asked
5. Test thoroughly — write test cases as part of solution

Sample Google-style problem walkthrough:

Problem: "Given a list of meeting times, find minimum conference rooms needed."

Step 1: Clarify
"Are meetings represented as [start, end]? Can they overlap at exact endpoints?"

Step 2: Approach
"Two approaches:
 1. Min-heap of end times — O(n log n)
 2. Event sweep with +1/-1 — O(n log n)
 I'll use the heap approach — more intuitive."

Step 3: Code
import heapq
def minMeetingRooms(intervals):
    intervals.sort()
    heap = []
    for start, end in intervals:
        if heap and heap[0] <= start:
            heapq.heapreplace(heap, end)
        else:
            heapq.heappush(heap, end)
    return len(heap)

Step 4: Test
"Input: [[0,30],[5,10],[15,20]]
 After [0,30]: heap=[30]
 After [5,10]: no room free (30>5), heap=[10,30]
 After [15,20]: [10]<=15, replace: heap=[20,30]
 Return 2. Correct."

Step 5: Complexity
"O(n log n) for sort + O(n log k) for heap ops where k <= n.
 Space: O(n) for heap."

Google Coding Rubric

Prepare These Google-Favourite Topics

Ranked by frequency:

1. Graph BFS/DFS (islands, paths, topological)
2. Dynamic programming (intervals, 2D, memo)
3. Sliding window / two pointers
4. Binary search on answer
5. String manipulation
6. Design questions (LRU, Trie)