Mock Week 4 — Full Company Simulation for Google, Meta, and Amazon

Overview#

Google, Meta, and Amazon grade coding interviews on fundamentally different axes. Google rewards multiple approaches and clean abstractions. Meta rewards speed and working code first. Amazon rewards correctness plus explicit Leadership Principles alignment. Practicing generic mocks without adapting communication style to the target company is one of the most common preparation mistakes.

Why This Matters#

Candidates who pass Google often fail Amazon for not framing decisions around ownership and customer impact. Candidates who pass Meta often fail Google for jumping to code without discussing multiple approaches first. Company fluency — adapting your communication style to the company's evaluation rubric — is a learnable skill that week 4 specifically builds.

FAANG mock interview preparation at week 4 means running one full simulation per target company in your priority list. If Google is your first choice, run the Google simulation twice. Each simulation must match the real round count, time per round, and the company's published evaluation rubric.

The week 4 target is a full mock pass: correct code, company-matched communication style, and a clean complexity statement before the interviewer asks.

Company Format Reference#

Core Framework — Token Bucket Rate Limiter (Amazon Staple)#

import time
from collections import defaultdict, deque
 
class TokenBucketLimiter:
    def __init__(self, capacity, refill_rate):
        self.capacity = capacity
        self.rate = refill_rate      # tokens per second
        self.tokens = {}
        self.last_refill = {}
 
    def allow(self, user_id):
        now = time.time()
        if user_id not in self.tokens:
            self.tokens[user_id] = self.capacity
            self.last_refill[user_id] = now
        elapsed = now - self.last_refill[user_id]
        self.tokens[user_id] = min(
            self.capacity,
            self.tokens[user_id] + elapsed * self.rate,
        )
        self.last_refill[user_id] = now
        if self.tokens[user_id] >= 1:
            self.tokens[user_id] -= 1
            return True
        return False
 
 
# Sliding window log — higher precision, more memory
class SlidingWindowLimiter:
    def __init__(self, max_requests, window_seconds):
        self.max = max_requests
        self.window = window_seconds
        self.logs = defaultdict(deque)
 
    def allow(self, user_id):
        now = time.time()
        log = self.logs[user_id]
        while log and now - log[0] > self.window:
            log.popleft()
        if len(log) < self.max:
            log.append(now)
            return True
        return False

class TokenBucketLimiter {
  constructor(capacity, refillRate) {
    this.capacity = capacity;
    this.rate = refillRate;
    this.tokens = new Map();
    this.lastRefill = new Map();
  }
 
  allow(userId) {
    const now = Date.now() / 1000;
    if (!this.tokens.has(userId)) {
      this.tokens.set(userId, this.capacity);
      this.lastRefill.set(userId, now);
    }
    const elapsed = now - this.lastRefill.get(userId);
    const refilled = Math.min(this.capacity, this.tokens.get(userId) + elapsed * this.rate);
    this.tokens.set(userId, refilled);
    this.lastRefill.set(userId, now);
    if (this.tokens.get(userId) >= 1) {
      this.tokens.set(userId, this.tokens.get(userId) - 1);
      return true;
    }
    return false;
  }
}

Time: O(1) per allow check | Space: O(U) where U is unique active users

Google Simulation — 45 Minutes Per Round#

Round 1 problem: text justification. Expectation: clean code that handles last-line left-alignment and single-word lines correctly. Follow-up: how does this scale if words arrive as a stream?

Round 2 problem: find all anagram starting indices in a string. Expectation: sliding window with a frequency map, O(n). Follow-up: what if the pattern can contain wildcard characters?

Google evaluation criteria:

• General coding — clean, correct code with meaningful variable names
• Algorithm knowledge — identifies optimal approach and explains why
• Communication — explains reasoning throughout, not just at the end
• Testing — proactively enumerates edge cases before declaring done

Google-specific communication style:

• Ask "Are there constraints on input size?" before writing any code
• Explicitly say "Approach 1 is X, approach 2 is Y — I will use Y because..."
• Write helper functions rather than one large monolithic function
• State complexity before the interviewer asks for it

Meta Simulation — 35 Minutes, 2 Problems Per Round#

Round 1: Problem A is easy (target 12 minutes), Problem B is medium (target 20 minutes).

Round 2: Problem A is medium (target 15 minutes), Problem B is medium-hard (target 18 minutes).

Meta evaluation criteria:

• Problem-solving speed — Meta values efficient time use above all
• Code quality — aims for no bugs on first submission
• Communication — clear and direct, not verbose
• Impact mindset — explicitly ask "What does this look like at 1 billion users?"

Meta-specific communication style:

• Jump to working code fast — optimize after, not before
• Note trade-offs explicitly: "Token bucket is simpler but less precise than sliding window"
• Favor simple elegant solutions over clever complex ones
• Always end with a scale consideration even when not prompted

Amazon Simulation — 30 Minutes Coding, 15 Minutes Behavioral#

Coding problem: token bucket rate limiter (see implementation above).

Behavioral prompt: "Tell me about a time you made a decision with incomplete data." Map to Bias for Action and Are Right A Lot.

Amazon evaluation criteria:

• Correctness — all edge cases including empty input, single user, burst traffic
• Leadership Principles alignment — every behavioral answer maps to one or two LPs explicitly
• Scalability thinking — "For distributed deployment, I would use Redis with a Lua atomic script"
• Ownership language — "I would be responsible for monitoring the rate limiter in production"

Common Mistakes#

• Using identical communication style across all three companies
• At Google: jumping to code without discussing two approaches
• At Meta: spending too long optimizing before the working solution exists
• At Amazon: giving behavioral answers without explicitly naming the Leadership Principle
• At any company: not stating complexity before being asked

Interview Tips#

• Practice the token bucket rate limiter on a plain text editor until you can write it from memory in under 8 minutes
• For Google: say "Let me discuss two approaches before I choose one" as an explicit opening move
• For Meta: use Python for its concise syntax — fewer boilerplate lines
• For Amazon: every behavioral answer closes with "which aligns with Deliver Results because..."
• Target 30 percent talking, 70 percent thinking-and-coding to avoid either rushing or over-explaining

Key Takeaways#

• Google rewards multiple approaches discussed before coding and clean modular abstractions
• Meta rewards speed — working code first, then optimization, then scale discussion
• Amazon rewards correctness plus explicit Leadership Principles alignment in every answer
• Token bucket is the canonical FAANG rate limiter: O(1) per check, O(U) space
• Sliding window log is the higher-precision alternative but uses more memory per user
• Company fluency means adapting communication style to the company's evaluation rubric, not just the algorithm
• Run at least one full simulation per priority company before scheduling real loops
• Week 4 is the dress rehearsal — treat it with the same intensity as the real interview