Company-Tagged DSA Problems — FAANG Interview Strategy Guide 2026

Problem Statement#

Plan a focused four to eight week study program that aligns DSA practice with the actual hiring patterns at Google, Meta, and Amazon — instead of grinding LeetCode randomly.

Constraints:

• Time budget: 4 to 8 weeks of consistent practice
• Must cover company-specific patterns, not just topic frequency
• Must include behavioural alignment for Amazon Leadership Principles

Input:  Candidate targeting Google + Meta + Amazon
Output: Weekly plan, pattern coverage, mock-interview cadence

Input:  Candidate with 2 weeks before Amazon onsite
Output: Top 10 Amazon problems + system design + LP stories

Why This Problem Matters#

This is the meta-guide for the entire dsa-company-problems series on webcoderspeed.com. Candidates who treat all FAANG interviews as one undifferentiated bucket waste preparation time. Google interviewers expect deep algorithmic justification, Meta interviewers reward elegant code with clean communication, and Amazon interviewers map every solution to a Leadership Principle. Knowing this lets you allocate study hours where they convert into offers.

The Core Insight#

Each big tech interview loop has a stable distribution of patterns that repeats across cohorts. Public sources like LeetCode company tags, Glassdoor reports, and the levels.fyi forum show consistent topic clusters. Google leans into graph algorithms, dynamic programming, and sliding window. Meta leans into trees, strings, and design-of-iterator style problems. Amazon leans into BFS, heaps, union-find, and design problems framed around scale.

The second insight is that company interviewers also weight non-coding signals differently. Google scores correctness and complexity rigour above all else. Meta scores communication and code quality. Amazon scores judgement, ownership, and how you handle ambiguity — all expressed through behavioural answers stitched into your coding response.

A focused plan exploits both of these biases at once.

Visual Dry Run#

Solution (Optimal)#

class StudyPlan:
    def __init__(self, target_companies, weeks):
        self.target = target_companies
        self.weeks = weeks
        self.patterns = {
            "google": ["graph", "dp", "sliding-window", "binary-search"],
            "meta":   ["tree", "string", "prefix-sum", "design"],
            "amazon": ["bfs", "heap", "union-find", "design"],
        }
 
    def schedule(self):
        plan = []
        for week in range(1, self.weeks + 1):
            focus = self._focus_for(week)
            plan.append((week, focus))
        return plan
 
    def _focus_for(self, week):
        bank = []
        for company in self.target:
            bank.extend(self.patterns[company])
        return bank[(week - 1) % len(bank)]

class StudyPlan {
    constructor(targetCompanies, weeks) {
        this.target = targetCompanies;
        this.weeks = weeks;
        this.patterns = {
            google: ["graph", "dp", "sliding-window", "binary-search"],
            meta:   ["tree", "string", "prefix-sum", "design"],
            amazon: ["bfs", "heap", "union-find", "design"],
        };
    }
    schedule() {
        const bank = this.target.flatMap(c => this.patterns[c]);
        const plan = [];
        for (let w = 1; w <= this.weeks; w++) {
            plan.push([w, bank[(w - 1) % bank.length]]);
        }
        return plan;
    }
}

Time: O(W) — one rotation per week Space: O(P) — pattern bank size

Common Mistakes#

• Studying topics by raw LeetCode frequency instead of company tag
• Skipping behavioural rehearsal for Amazon
• Not doing timed mock interviews in the final two weeks
• Memorising solutions without understanding why each pattern wins
• Ignoring system design even when the role expects it

Interview Tips#

• For Google, always state time and space complexity proactively
• For Meta, narrate your code as you write it, prefer iterative clarity
• For Amazon, anchor your solution to a Leadership Principle by name
• Keep a personal pattern journal — one page per pattern you internalise
• Schedule one full mock loop in week 7 before real interviews

Follow-up Questions#

• Which pattern shows up most often across all three companies? (Hint: BFS and DFS)
• How would you adapt this plan for Apple or Microsoft? (Hint: add OS and concurrency)
• How do new-grad versus senior loops differ? (Hint: senior loops add system design and scope rounds)
• What changes for ML or infra rounds? (Hint: shift weeks 5 to 6 toward domain content)
• How do you decide when to stop preparing and start interviewing? (Hint: target 70 percent solve rate on fresh medium problems)

Key Takeaways#

• Company-specific FAANG prep beats generic LeetCode grinding
• Google rewards depth — derive optimisations and prove correctness
• Meta rewards elegance — clean code and communication win signal
• Amazon rewards judgement — every answer should connect to a Leadership Principle
• A six to eight week plan with weekly themes covers 80 percent of the question surface
• Mock interviews in the final two weeks compound more than extra problem solving
• This series covers 23 representative problems plus a master recap