Greedy Questions at Turing: What to Expect

Greedy algorithms are a small but critical part of the Turing coding interview. With 4 out of 40 questions typically dedicated to this paradigm, you cannot afford to ignore them. While the count may seem low, these questions are often high-impact. They test your ability to identify optimal substructure and make locally optimal choices that lead to a globally optimal solution—a key skill for designing efficient systems. Failing a greedy problem can be a significant setback in your overall score.

What to Expect — Types of Problems

Turing's greedy questions tend to focus on practical, recognizable patterns rather than obscure mathematical puzzles. You can expect problems in these core categories:

1. Interval Scheduling: Problems involving selecting non-overlapping intervals, such as meeting rooms or tasks, to maximize count or minimize conflicts.
2. Coin Change / Making Change: Given coin denominations, find the minimum number of coins to make a target amount (when the greedy approach is valid, unlike the dynamic programming version).
3. Assignments & Pairings: Problems like assigning cookies to children or matching speed to efficiency to maximize or minimize a metric.
4. Jump Game Variants: Determining the minimum number of jumps to reach the end of an array.

The challenge is rarely in coding complexity; it's in proving to yourself that a greedy approach will work for the given problem constraints.

How to Prepare — Study Tips with One Code Example

Your preparation should focus on pattern recognition and validation. Don't just memorize solutions. For each problem, ask: "Why is the greedy choice optimal here?" Practice the following steps:

1. Identify the greedy choice: What is the local optimal decision at each step? (e.g., always pick the earliest ending interval).
2. Prove safety: Argue that this choice is part of some optimal solution.
3. Implement efficiently: This often involves sorting first, then making a single pass.

A classic example is the "Meeting Rooms II" style problem: given intervals, find the minimum number of rooms (or resources) needed to avoid conflicts. The efficient greedy approach uses a min-heap to track end times.

import heapq

def min_meeting_rooms(intervals):
    if not intervals:
        return 0

    intervals.sort(key=lambda x: x[0])  # Sort by start time
    end_times = []  # min-heap
    heapq.heappush(end_times, intervals[0][1])

    for interval in intervals[1:]:
        start, end = interval
        # If the room due to free the earliest is free before this start time, reuse it
        if end_times[0] <= start:
            heapq.heappop(end_times)
        # Assign a new room (or the reused one)
        heapq.heappush(end_times, end)

    return len(end_times)

Recommended Practice Order

Build your understanding progressively:

1. Start with foundational problems: Assign Cookies, Lemonade Change.
2. Move to interval problems: Non-overlapping Intervals, Minimum Number of Arrows to Burst Balloons.
3. Tackle jump problems: Jump Game I & II.
4. Conclude with advanced greedy: Task Scheduler, Gas Station.

Master these patterns. In the interview, clearly articulate your reasoning before coding. A correct greedy solution is typically concise and efficient, leaving a strong impression.

Practice Greedy at Turing