FAANG Interview Preparation Guide — Complete Step-by-Step Plan

from datetime import datetime, timedelta

class FAANGStudyPlan:
    def __init__(self, start_date):
        self.start = datetime.strptime(start_date, "%Y-%m-%d")
        self.weeks = {}

    def add_week(self, week_num, focus, problems_per_day, topics):
        week_start = self.start + timedelta(weeks=week_num - 1)
        self.weeks[week_num] = {
            "focus": focus,
            "problems": problems_per_day * 7,
            "topics": topics,
            "start": week_start.strftime("%b %d")
        }

    def summary(self):
        for week, details in sorted(self.weeks.items()):
            print(f"Week {week:2d} ({details['start']}): {details['focus']}")
            print(f"     {details['problems']} problems | Topics: {', '.join(details['top'])}")

plan = FAANGStudyPlan("2026-07-01")
plan.add_week(1, "Arrays & Strings", 2, ["two pointers", "sliding window", "prefix sum"])
plan.add_week(2, "Linked Lists & Stacks", 2, ["reversal", "cycle detection", "monotonic stack"])
plan.add_week(3, "Trees & Graphs", 2, ["BFS", "DFS", "binary tree traversals"])
plan.add_week(4, "Recursion & Backtracking", 2, ["subsets", "permutations", "N-Queens"])
plan.add_week(5, "Dynamic Programming I", 2, ["0/1 knapsack", "LCS", "coin change"])
plan.add_week(6, "Dynamic Programming II", 2, ["LIS", "edit distance", "matrix DP"])
plan.add_week(7, "Sorting & Searching", 2, ["quicksort", "binary search", "rotated array"])
plan.add_week(8, "Heaps & Priority Queues", 2, ["top K", "median of stream", "merge K lists"])
plan.add_week(9, "System Design I", 1, ["URL shortener", "key-value store", "rate limiter"])
plan.add_week(10, "System Design II", 1, ["chat system", "news feed", "distributed cache"])
plan.add_week(11, "Mock Interviews", 3, ["full coding mocks", "system design mocks", "behavioral"])
plan.add_week(12, "Review & Gaps", 2, ["weak areas", "company-specific prep", "behavioral stories"])
plan.summary()

company_focus = {
    "Google": {
        "coding": "hardest, focuses on algorithmic thinking",
        "system_design": "medium-high, practical systems",
        "behavioral": "lightweight, 1 round",
        "extra": "googleyness and leadership"
    },
    "Meta": {
        "coding": "medium-hard, speed matters",
        "system_design": "high, distributed systems focus",
        "behavioral": "heavy, 2 rounds on execution",
        "extra": "product sense and speed"
    },
    "Amazon": {
        "coding": "medium, practical problems",
        "system_design": "medium, scalability focused",
        "behavioral": "heaviest, leadership principles",
        "extra": "bar raiser round"
    },
    "Apple": {
        "coding": "hard, depth over breadth",
        "system_design": "medium-high, domain-specific",
        "behavioral": "medium, cross-functional focus",
        "extra": "domain expertise matters"
    },
    "Netflix": {
        "coding": "medium, clean code valued",
        "system_design": "high, microservices focus",
        "behavioral": "heavy, culture and judgment",
        "extra": "freedom and responsibility"
    }
}

for company, details in company_focus.items():
    print(f"{company}:")
    for aspect, desc in details.items():
        print(f"  {aspect}: {desc}")
    print()

import java.util.*;

public class CompanyFocus {
    public static void main(String[] args) {
        Map<String, String> prepRatios = new HashMap<>();
        prepRatios.put("Google", "80% coding, 15% system design, 5% behavioral");
        prepRatios.put("Meta", "50% coding, 30% system design, 20% behavioral");
        prepRatios.put("Amazon", "40% coding, 20% system design, 40% behavioral");
        prepRatios.forEach((c, r) -> System.out.println(c + ": " + r));
    }
}

class ResumeCheck:
    def __init__(self):
        self.rules = [
            ("Quantify achievements", "increased x by y%", True),
            ("Single page", "length <= 1 page", True),
            ("Role-specific keywords", "target role appears 5+ times", True),
            ("Action verbs", "led, designed, built, optimized", True),
            ("Degree + GPA", "if > 3.5 include GPA", True),
            ("No buzzword stuffing", "avoid 90s tech", True),
            ("Reverse chronological", "newest first", True),
        ]

    def check(self, resume_text):
        issues = []
        for rule, hint, required in self.rules:
            issues.append({"rule": rule, "hint": hint, "pass": False})
        return issues

checker = ResumeCheck()
print([r["rule"] for r in checker.check("")])

#include <iostream>
#include <string>
#include <vector>
using namespace std;

struct ResumeRule {
    string rule;
    string hint;
};

int main() {
    vector<ResumeRule> rules = {
        {"Quantify results", "Use numbers: increased, reduced, led"},
        {"Single page", "Keep to one page for <10 years experience"},
        {"Keywords", "Match job description keywords exactly"},
        {"Action verbs", "Start bullets with strong action verbs"}
    };
    for (auto& r : rules) {
        cout << r.rule << ": " << r.hint << endl;
    }
    return 0;
}