Structured Coding Practice: Amazon-Tagged Problems with Management Perspective

🎯 Coding Practice Framework for L6/L7 Engineering Managers

Management-Focused Coding Practice

This structured coding practice framework provides Amazon-tagged problems, algorithmic patterns, and time management strategies specifically designed for L6/L7 engineering managers who need to demonstrate coding competency while emphasizing leadership perspective.

📊 L6/L7 Coding Expectations

Key Differences from Individual Contributor Interviews

L6 Engineering Manager Coding: - Focus: Clean, maintainable code with team scalability considerations - Complexity: LeetCode Medium problems (80%), Easy problems (20%) - Time Limit: 30-45 minutes per problem - Emphasis: Code quality, team collaboration, production readiness

L7 Senior Engineering Manager Coding: - Focus: Architecture-aware solutions with system design integration - Complexity: LeetCode Medium problems (60%), Hard problems (40%) - Time Limit: 25-40 minutes per problem - Emphasis: Strategic thinking, platform considerations, mentoring approach

Management Perspective Integration

• Code Review Mindset: Write code you'd approve in review
• Mentoring Approach: Explain your thought process for teaching others
• Production Readiness: Consider error handling, edge cases, and maintainability
• Team Scalability: Solutions that multiple engineers could work on together

🏷️ Amazon-Tagged Problem Categories

Core Data Structures and Algorithms

Array and String Manipulation (25% of coding interviews)

Essential Problems (L6 Focus):

1. Two Sum (Easy) - Amazon Frequency: Very High

def two_sum(nums, target):
    """
    Management Perspective:
    - Classic problem for teaching hash table optimization
    - Good example of trading space for time complexity
    - Common in phone screens and warm-up questions

    Production Considerations:
    - Input validation for edge cases
    - Memory optimization for large arrays
    - Thread safety if used in concurrent environment
    """
    num_map = {}

    for i, num in enumerate(nums):
        complement = target - num
        if complement in num_map:
            return [num_map[complement], i]
        num_map[num] = i

    return []  # No solution found

# Time: O(n), Space: O(n)
# Management Teaching Point: Hash table pattern appears in many problems

2. Longest Substring Without Repeating Characters (Medium) - Amazon Frequency: High

def length_of_longest_substring(s):
    """
    Management Perspective:
    - Excellent sliding window pattern demonstration
    - Shows optimization from brute force to efficient solution
    - Common pattern in many Amazon problems

    Team Discussion Points:
    - Multiple valid approaches (set vs array tracking)
    - Trade-offs between readability and performance
    - Edge case handling strategy
    """
    char_set = set()
    left = max_length = 0

    for right in range(len(s)):
        # Shrink window until no duplicates
        while s[right] in char_set:
            char_set.remove(s[left])
            left += 1

        char_set.add(s[right])
        max_length = max(max_length, right - left + 1)

    return max_length

# Time: O(n), Space: O(min(m,n)) where m is charset size
# Management Teaching Point: Sliding window pattern

3. Merge Intervals (Medium) - Amazon Frequency: High

def merge_intervals(intervals):
    """
    Management Perspective:
    - Real-world application in scheduling, resource allocation
    - Good problem for discussing sorting strategy
    - Demonstrates clean algorithm implementation

    Production Considerations:
    - Input validation for malformed intervals
    - Memory efficiency for large datasets
    - Extensible design for different merge criteria
    """
    if not intervals:
        return []

    # Sort by start time - key decision point for team discussion
    intervals.sort(key=lambda x: x[0])
    merged = [intervals[0]]

    for current in intervals[1:]:
        last_merged = merged[-1]

        if current[0] <= last_merged[1]:  # Overlapping
            # Merge intervals
            merged[-1] = [last_merged[0], max(last_merged[1], current[1])]
        else:
            merged.append(current)

    return merged

# Time: O(n log n), Space: O(1) excluding output
# Management Teaching Point: Sorting + greedy algorithm pattern

Tree and Graph Algorithms (20% of coding interviews)

Essential Problems (L6/L7 Focus):

4. Binary Tree Level Order Traversal (Medium) - Amazon Frequency: High

from collections import deque

def level_order(root):
    """
    Management Perspective:
    - Fundamental BFS pattern used in many tree problems
    - Good for discussing iterative vs recursive approaches
    - Demonstrates queue usage and level-by-level processing

    Team Architecture Discussion:
    - BFS vs DFS trade-offs in real systems
    - Memory usage patterns in tree traversal
    - Application to hierarchical data structures
    """
    if not root:
        return []

    result = []
    queue = deque([root])

    while queue:
        level_size = len(queue)
        current_level = []

        # Process all nodes at current level
        for _ in range(level_size):
            node = queue.popleft()
            current_level.append(node.val)

            if node.left:
                queue.append(node.left)
            if node.right:
                queue.append(node.right)

        result.append(current_level)

    return result

# Time: O(n), Space: O(w) where w is max width
# Management Teaching Point: BFS pattern, level-by-level processing

5. Validate Binary Search Tree (Medium) - Amazon Frequency: Very High

def is_valid_bst(root):
    """
    Management Perspective:
    - Classic problem for teaching BST properties
    - Multiple solution approaches for team discussion
    - Important for understanding data structure invariants

    Code Review Teaching Points:
    - Boundary handling (Integer.MIN_VALUE issues)
    - Recursive solution elegance vs iterative clarity
    - Importance of comprehensive test cases
    """
    def validate(node, min_val, max_val):
        if not node:
            return True

        if node.val <= min_val or node.val >= max_val:
            return False

        return (validate(node.left, min_val, node.val) and 
                validate(node.right, node.val, max_val))

    return validate(root, float('-inf'), float('inf'))

# Time: O(n), Space: O(h) where h is height
# Management Teaching Point: Recursive validation with bounds

6. Number of Islands (Medium) - Amazon Frequency: Very High

def num_islands(grid):
    """
    Management Perspective:
    - Excellent DFS/BFS application problem
    - Real-world analogy for connected components
    - Good for discussing grid traversal patterns

    System Design Connection:
    - Similar to finding connected components in distributed systems
    - Scalability considerations for large grids
    - Parallel processing possibilities
    """
    if not grid or not grid[0]:
        return 0

    rows, cols = len(grid), len(grid[0])
    islands = 0

    def dfs(r, c):
        # Mark current cell as visited by setting to '0'
        if (r < 0 or r >= rows or c < 0 or c >= cols or 
            grid[r][c] == '0'):
            return

        grid[r][c] = '0'  # Mark as visited

        # Explore all 4 directions
        dfs(r + 1, c)
        dfs(r - 1, c)
        dfs(r, c + 1)
        dfs(r, c - 1)

    for r in range(rows):
        for c in range(cols):
            if grid[r][c] == '1':
                islands += 1
                dfs(r, c)

    return islands

# Time: O(m*n), Space: O(m*n) worst case for recursion stack
# Management Teaching Point: DFS for connected components

Dynamic Programming (15% of coding interviews)

Essential Problems (L7 Focus):

7. Climbing Stairs (Easy) - Amazon Frequency: Medium

def climb_stairs(n):
    """
    Management Perspective:
    - Perfect introduction to DP concepts for team training
    - Shows evolution from recursion to optimized iterative solution
    - Foundation for understanding more complex DP problems

    Teaching Progression:
    1. Recursive solution (exponential time)
    2. Memoized recursion (top-down DP)
    3. Iterative solution (bottom-up DP)
    4. Space-optimized version
    """
    if n <= 2:
        return n

    # Space-optimized DP approach
    prev2, prev1 = 1, 2

    for i in range(3, n + 1):
        current = prev1 + prev2
        prev2, prev1 = prev1, current

    return prev1

# Time: O(n), Space: O(1)
# Management Teaching Point: DP optimization progression

8. Coin Change (Medium) - Amazon Frequency: High

def coin_change(coins, amount):
    """
    Management Perspective:
    - Classic optimization problem with real-world applications
    - Good for discussing greedy vs DP approaches
    - Demonstrates bottom-up DP thinking

    Business Application Discussion:
    - Currency conversion optimization
    - Resource allocation problems
    - Cost minimization scenarios
    """
    # DP array: dp[i] = minimum coins to make amount i
    dp = [float('inf')] * (amount + 1)
    dp[0] = 0  # Base case: 0 coins needed for amount 0

    for i in range(1, amount + 1):
        for coin in coins:
            if coin <= i:
                dp[i] = min(dp[i], dp[i - coin] + 1)

    return dp[amount] if dp[amount] != float('inf') else -1

# Time: O(amount * len(coins)), Space: O(amount)
# Management Teaching Point: Bottom-up DP with optimization

System Design Coding (10% of coding interviews)

Essential Problems (L6/L7 Focus):

9. LRU Cache (Medium) - Amazon Frequency: Very High

class LRUCache:
    """
    Management Perspective:
    - Perfect intersection of coding and system design
    - Demonstrates understanding of cache eviction policies
    - Real-world application in distributed systems

    Architecture Discussion Points:
    - Trade-offs between different data structure combinations
    - Thread safety considerations for production use
    - Monitoring and metrics for cache performance
    - Scaling considerations for distributed cache
    """

    class Node:
        def __init__(self, key=0, value=0):
            self.key = key
            self.value = value
            self.prev = None
            self.next = None

    def __init__(self, capacity):
        self.capacity = capacity
        self.cache = {}  # key -> node mapping

        # Create dummy head and tail nodes
        self.head = self.Node()
        self.tail = self.Node()
        self.head.next = self.tail
        self.tail.prev = self.head

    def _add_node(self, node):
        """Add node right after head"""
        node.prev = self.head
        node.next = self.head.next

        self.head.next.prev = node
        self.head.next = node

    def _remove_node(self, node):
        """Remove an existing node"""
        prev_node = node.prev
        next_node = node.next

        prev_node.next = next_node
        next_node.prev = prev_node

    def _move_to_head(self, node):
        """Move node to head (mark as recently used)"""
        self._remove_node(node)
        self._add_node(node)

    def _pop_tail(self):
        """Remove the last node (LRU)"""
        lru_node = self.tail.prev
        self._remove_node(lru_node)
        return lru_node

    def get(self, key):
        node = self.cache.get(key)
        if node:
            # Mark as recently used
            self._move_to_head(node)
            return node.value
        return -1

    def put(self, key, value):
        node = self.cache.get(key)

        if node:
            # Update existing node
            node.value = value
            self._move_to_head(node)
        else:
            # Add new node
            new_node = self.Node(key, value)

            if len(self.cache) >= self.capacity:
                # Remove LRU
                lru = self._pop_tail()
                del self.cache[lru.key]

            self.cache[key] = new_node
            self._add_node(new_node)

# Time: O(1) for both get and put, Space: O(capacity)
# Management Teaching Point: Hash table + doubly linked list combination

10. Design Rate Limiter (Medium) - Amazon Frequency: High

import time
from collections import defaultdict, deque

class RateLimiter:
    """
    Management Perspective:
    - Critical for API design and system reliability
    - Multiple algorithm choices for different requirements
    - Production considerations for distributed systems

    Algorithm Discussion:
    1. Token Bucket (smooth rate limiting)
    2. Sliding Window Log (precise but memory intensive)
    3. Sliding Window Counter (approximation with lower memory)
    4. Fixed Window (simple but can have bursts)
    """

    def __init__(self, requests_per_minute=60):
        self.requests_per_minute = requests_per_minute
        self.window_size = 60  # 1 minute in seconds
        self.requests = defaultdict(deque)

    def is_allowed(self, user_id):
        """Sliding window log implementation"""
        current_time = time.time()
        user_requests = self.requests[user_id]

        # Remove requests outside current window
        while user_requests and user_requests[0] <= current_time - self.window_size:
            user_requests.popleft()

        # Check if under limit
        if len(user_requests) < self.requests_per_minute:
            user_requests.append(current_time)
            return True

        return False

class TokenBucketRateLimiter:
    """
    Alternative implementation using Token Bucket algorithm
    Better for allowing bursts while maintaining average rate
    """

    def __init__(self, rate, bucket_size):
        self.rate = rate  # tokens per second
        self.bucket_size = bucket_size
        self.tokens = bucket_size
        self.last_refill = time.time()

    def is_allowed(self, tokens_requested=1):
        now = time.time()
        # Add tokens based on elapsed time
        tokens_to_add = (now - self.last_refill) * self.rate
        self.tokens = min(self.bucket_size, self.tokens + tokens_to_add)
        self.last_refill = now

        if self.tokens >= tokens_requested:
            self.tokens -= tokens_requested
            return True

        return False

# Management Teaching Point: Different algorithms for different requirements

🎯 Problem-Solving Patterns for Amazon Interviews

Pattern 1: Two Pointers Technique

When to Use: - Array or string problems requiring pair finding - Optimizing nested loops to single pass - Palindrome or symmetry checks

Amazon Problems: - Container With Most Water - 3Sum - Remove Duplicates from Sorted Array

Template:

def two_pointer_template(arr):
    left, right = 0, len(arr) - 1

    while left < right:
        # Process current pair
        if condition_met(arr[left], arr[right]):
            # Process and move both pointers
            left += 1
            right -= 1
        elif left_should_move:
            left += 1
        else:
            right -= 1

    return result

Pattern 2: Sliding Window

When to Use: - Substring or subarray problems - Finding optimal window size - Maintaining window properties

Amazon Problems: - Minimum Window Substring - Longest Substring Without Repeating Characters - Find All Anagrams in a String

Template:

def sliding_window_template(s):
    window_map = {}
    left = 0
    result = []

    for right in range(len(s)):
        # Expand window
        char = s[right]
        window_map[char] = window_map.get(char, 0) + 1

        # Contract window if needed
        while window_invalid():
            left_char = s[left]
            window_map[left_char] -= 1
            if window_map[left_char] == 0:
                del window_map[left_char]
            left += 1

        # Update result if window is valid
        if window_valid():
            result.append(get_current_result())

    return result

Pattern 3: DFS/BFS for Trees and Graphs

When to Use: - Tree traversal problems - Graph connectivity problems - Path finding and exploration

Amazon Problems: - Binary Tree Right Side View - Word Ladder - Clone Graph

DFS Template:

def dfs_template(root):
    if not root:
        return base_case_result

    # Process current node
    current_result = process(root)

    # Recursively process children
    left_result = dfs_template(root.left)
    right_result = dfs_template(root.right)

    # Combine results
    return combine(current_result, left_result, right_result)

Pattern 4: Dynamic Programming

When to Use: - Optimization problems (min/max) - Counting problems - Decision making with multiple choices

Amazon Problems: - Best Time to Buy and Sell Stock - Word Break - Longest Increasing Subsequence

DP Template:

def dp_template(input_data):
    n = len(input_data)
    # Initialize DP table
    dp = [0] * (n + 1)
    dp[0] = base_case

    for i in range(1, n + 1):
        for prev_state in get_previous_states(i):
            dp[i] = optimize(dp[i], dp[prev_state] + transition_cost)

    return dp[n]

⏱️ Time Management Strategy

45-Minute Coding Interview Breakdown

Minutes 0-5: Problem Understanding - Read problem carefully and ask clarifying questions - Identify input/output format and constraints - Discuss edge cases and assumptions - Choose appropriate algorithm pattern

Minutes 5-10: High-Level Approach - Explain your solution approach - Discuss time and space complexity - Get interviewer buy-in before coding - Identify potential optimizations

Minutes 10-35: Implementation - Write clean, readable code - Implement core logic first - Add error handling and edge cases - Test with example inputs

Minutes 35-40: Testing and Optimization - Walk through code with test cases - Identify and fix any bugs - Discuss potential optimizations - Consider alternative approaches

Minutes 40-45: Discussion and Follow-up - Discuss production considerations - Talk about scalability and maintenance - Address any interviewer questions - Demonstrate management perspective

Management Perspective Integration

During Problem Solving:

"As a manager, I'd approach this problem by first understanding the business requirements..."

"This solution would be easy for my team to understand and maintain because..."

"In a code review, I'd look for these specific aspects..."

"For production deployment, we'd need to consider..."

Code Quality Focus: - Clear variable names and function signatures - Appropriate comments for complex logic - Error handling for edge cases - Modular design that teammates could extend

📚 Amazon-Specific Problem Categories

High-Frequency Amazon Problems (Must Practice)

Array/String (Practice 15-20 problems): 1. Two Sum (Easy) ⭐⭐⭐⭐⭐ 2. Longest Substring Without Repeating Characters (Medium) ⭐⭐⭐⭐⭐ 3. Merge Intervals (Medium) ⭐⭐⭐⭐⭐ 4. Product of Array Except Self (Medium) ⭐⭐⭐⭐ 5. Group Anagrams (Medium) ⭐⭐⭐⭐ 6. Valid Parentheses (Easy) ⭐⭐⭐⭐ 7. Minimum Window Substring (Hard) ⭐⭐⭐⭐ 8. 3Sum (Medium) ⭐⭐⭐ 9. Container With Most Water (Medium) ⭐⭐⭐ 10. Rotate Array (Medium) ⭐⭐⭐

Trees/Graphs (Practice 10-15 problems): 1. Binary Tree Level Order Traversal (Medium) ⭐⭐⭐⭐⭐ 2. Validate Binary Search Tree (Medium) ⭐⭐⭐⭐⭐ 3. Number of Islands (Medium) ⭐⭐⭐⭐⭐ 4. Binary Tree Right Side View (Medium) ⭐⭐⭐⭐ 5. Lowest Common Ancestor of BST (Easy) ⭐⭐⭐⭐ 6. Word Ladder (Medium) ⭐⭐⭐⭐ 7. Clone Graph (Medium) ⭐⭐⭐ 8. Course Schedule (Medium) ⭐⭐⭐ 9. Serialize and Deserialize Binary Tree (Hard) ⭐⭐⭐ 10. Path Sum II (Medium) ⭐⭐⭐

Dynamic Programming (Practice 8-10 problems): 1. Climbing Stairs (Easy) ⭐⭐⭐⭐ 2. Coin Change (Medium) ⭐⭐⭐⭐⭐ 3. Longest Increasing Subsequence (Medium) ⭐⭐⭐⭐ 4. Word Break (Medium) ⭐⭐⭐⭐ 5. Best Time to Buy and Sell Stock (Easy) ⭐⭐⭐ 6. House Robber (Easy) ⭐⭐⭐ 7. Unique Paths (Medium) ⭐⭐⭐ 8. Edit Distance (Hard) ⭐⭐⭐

System Design Coding (Practice 5-8 problems): 1. LRU Cache (Medium) ⭐⭐⭐⭐⭐ 2. Design Rate Limiter (Medium) ⭐⭐⭐⭐⭐ 3. Implement Trie (Medium) ⭐⭐⭐⭐ 4. Design HashMap (Easy) ⭐⭐⭐ 5. Min Stack (Easy) ⭐⭐⭐ 6. Design Hit Counter (Medium) ⭐⭐⭐ 7. Design Log Storage System (Medium) ⭐⭐ 8. Design Search Autocomplete (Hard) ⭐⭐

🎯 Weekly Practice Schedule

Structured 12-Week Program

Weeks 1-3: Foundations - Week 1: Array and String basics (10 problems) - Week 2: Two pointers and sliding window (8 problems) - Week 3: Basic tree traversal (8 problems)

Weeks 4-6: Intermediate Patterns - Week 4: Graph algorithms (BFS/DFS) (8 problems) - Week 5: Basic dynamic programming (8 problems) - Week 6: Advanced string problems (8 problems)

Weeks 7-9: Advanced Topics - Week 7: Advanced tree problems (6 problems) - Week 8: Complex DP problems (6 problems) - Week 9: System design coding (6 problems)

Weeks 10-12: Interview Preparation - Week 10: Mixed problem sets, timed practice - Week 11: Mock interviews and weak area focus - Week 12: Final review and confidence building

Daily Practice Routine (6 days/week)

Monday: Algorithm Focus (60 minutes) - Solve 2 medium problems or 1 hard problem - Focus on pattern recognition - Write clean, production-ready code - Time limit: 30 minutes per problem

Tuesday: Pattern Review (45 minutes) - Review previous day's solutions - Implement alternative approaches - Study optimal solutions from others - Document patterns learned

Wednesday: Speed Practice (60 minutes) - Solve 3 easy problems in 45 minutes - Focus on implementation speed - Practice common patterns - Build muscle memory for syntax

Thursday: Mock Interview (60 minutes) - Full interview simulation - Practice explaining approach aloud - Get feedback on code quality - Work on communication skills

Friday: System Design Coding (60 minutes) - Focus on data structure design problems - Emphasize production considerations - Practice API design - Consider scalability aspects

Saturday: Review and Planning (30 minutes) - Review week's progress - Identify areas for improvement - Plan next week's focus areas - Update problem tracking spreadsheet

Sunday: Rest (optional light review)

📊 Progress Tracking and Assessment

Problem Solving Assessment Matrix

For Each Problem Track: - First Attempt: Time taken, correctness, approach used - Optimal Solution: Time/space complexity achieved - Code Quality: Readability, error handling, edge cases - Management Perspective: Production considerations discussed

Weekly Self-Assessment (Rate 1-5)

Technical Skills: - Problem pattern recognition: ___/5 - Algorithm implementation speed: ___/5 - Code quality and readability: ___/5 - Time and space complexity analysis: ___/5

Management Perspective: - Production readiness consideration: ___/5 - Team/code review mindset: ___/5 - Teaching and explanation ability: ___/5 - Strategic thinking integration: ___/5

Interview Readiness: - Communication during coding: ___/5 - Handling hints and feedback: ___/5 - Time management: ___/5 - Confidence and composure: ___/5

Monthly Milestone Assessments

Month 1 Goals: - [ ] Comfortable with 80% of easy problems - [ ] Solid understanding of basic patterns - [ ] Clean code writing habits established - [ ] Can explain approach clearly

Month 2 Goals: - [ ] Solving 70% of medium problems correctly - [ ] Pattern recognition becoming automatic - [ ] Integrating management perspective naturally - [ ] Mock interview performance improving

Month 3 Goals: - [ ] Consistent performance on medium problems - [ ] Attempting some hard problems successfully - [ ] Strong code quality and edge case handling - [ ] Interview-ready confidence level

💡 Management Perspective Integration Examples

Code Review Mindset Examples

Instead of: "This works." Say: "This solution is clean and maintainable. In a code review, I'd praise the clear variable naming and would suggest adding input validation for production use."

Instead of: "I chose HashMap for O(1) lookup." Say: "I chose HashMap because it gives us O(1) average case lookup, which is important for this algorithm's performance. I'd discuss with my team whether we need to handle worst-case O(n) scenarios in our specific use case."

Instead of: "The algorithm is O(n)." Say: "This O(n) solution scales well with our expected data size. For my team's context, this would handle our typical workload efficiently, and we could optimize further if we see performance issues in production."

Production Considerations to Mention

Error Handling:

def robust_solution(input_data):
    # Input validation - important for production
    if not input_data or not isinstance(input_data, list):
        raise ValueError("Invalid input: expected non-empty list")

    # Implementation with error handling
    try:
        result = core_algorithm(input_data)
        return result
    except Exception as e:
        # Log error for monitoring
        logger.error(f"Algorithm failed: {e}")
        return default_value

Monitoring and Metrics:

# Example of management perspective on monitoring
def solution_with_monitoring(data):
    start_time = time.time()

    result = algorithm(data)

    # Metrics that a manager would want to track
    execution_time = time.time() - start_time
    metrics.record_latency("algorithm_name", execution_time)
    metrics.increment_counter("algorithm_calls")

    return result

Scalability Considerations: - "For larger datasets, we might need to consider distributed processing" - "This solution works well for our current scale, but we should monitor performance as data grows" - "In production, we'd want to add caching for repeated queries"

🎯 Common Amazon Coding Interview Questions with Management Focus

Question: "How would you approach mentoring a junior developer on this problem?"

Strong Response Framework: 1. Start with Understanding: "I'd first ask them to explain their understanding of the problem" 2. Guide Discovery: "Rather than giving the solution, I'd ask leading questions to guide them to the pattern" 3. Encourage Multiple Approaches: "I'd have them think through brute force first, then optimize" 4. Focus on Learning: "The goal is building their pattern recognition, not just solving this problem"

Question: "How would you handle this in a code review?"

Strong Response Framework: 1. Positive Feedback: "I'd acknowledge what they did well first" 2. Constructive Suggestions: "Then suggest improvements with clear reasoning" 3. Educational Approach: "I'd use this as a teaching moment for the team" 4. Follow-up: "I'd schedule time to discuss patterns they could apply elsewhere"

Question: "What would you consider for production deployment?"

Strong Response Framework: 1. Testing Strategy: "Comprehensive unit tests and edge case coverage" 2. Performance Monitoring: "Metrics and alerting for latency and error rates" 3. Scalability Planning: "Load testing and capacity planning" 4. Rollback Plan: "Safe deployment strategy with quick rollback capability"

🚀 Getting Started: Week 1 Action Plan

Day 1-2: Assessment and Setup

• Take coding skills assessment with 5 representative problems
• Set up development environment and timer
• Choose problem tracking method (spreadsheet, Notion, etc.)
• Review algorithm patterns overview

Day 3-4: Foundation Building

• Solve Two Sum and explain management perspective
• Practice Longest Substring Without Repeating Characters
• Focus on clean code and clear communication
• Record yourself explaining solutions

Day 5-7: Pattern Recognition

• Complete 3 sliding window problems
• Review solutions and identify common patterns
• Practice explaining optimization thought process
• Schedule first mock interview for next week

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Coding Practice Success Formula

Consistent Practice (daily rhythm) + Pattern Recognition (algorithmic thinking) + Management Perspective (production mindset) + Clear Communication (teaching ability) = Coding Interview Success

Start with the Two Sum problem and focus on integrating management perspective from day one. Use our Mock Interview Framework to practice coding under realistic interview conditions.

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