Approach

To effectively answer the question about implementing a function to calculate the number of ways to climb stairs with a variable number of steps, follow this structured framework:

1. Understand the Problem: Define the parameters of the problem including the total number of stairs and the maximum steps that can be taken in one move.

2. Identify Base Cases: Determine the scenarios where the outcome is straightforward (e.g., 0 stairs or 1 stair).

3. Develop a Recursive Formula: Establish how to break down the problem using recursion or dynamic programming.

4. Implement the Solution: Write the code based on the formulated logic.

5. Test the Function: Validate the function with various inputs to ensure it works correctly.

Key Points

• Clarity on Requirements: Interviewers want to see your understanding of algorithms and your ability to solve problems using logical reasoning.

• Efficiency Matters: Aim for a solution that minimizes time and space complexity.

• Code Readability: Write clean, understandable code with proper comments.

• Explain Your Thought Process: During the interview, communicate your reasoning clearly.

Standard Response

Here is a comprehensive sample answer that incorporates the above approach.

def climb_stairs(n, max_steps):
 """
 Calculate the number of ways to climb to the top of the stairs.
 
 :param n: Total number of stairs
 :param max_steps: Maximum steps that can be taken at once
 :return: Number of ways to reach the top
 """
 
 # Base cases
 if n < 0:
 return 0
 if n == 0:
 return 1
 
 # Initialize a list to store the number of ways to reach each step
 dp = [0] * (n + 1)
 dp[0] = 1 # There's one way to stay at the ground (do nothing)

 # Fill the dp array
 for i in range(1, n + 1):
 for j in range(1, min(i, max_steps) + 1):
 dp[i] += dp[i - j]
 
 return dp[n]

# Example usage:
stairs = 5
max_steps_allowed = 2
print(climb_stairs(stairs, max_steps_allowed)) # Output: 8

• The function climbstairs takes two arguments: n, the total number of stairs, and maxsteps, the maximum steps that can be taken in one move.

• It checks for base cases: if there are negative stairs (returns 0) or if you are at the ground level (returns 1).

• It initializes a dynamic programming array dp to cache the number of ways to reach each stair.

• The nested loop fills the dp array by summing the ways to reach each stair considering the last max_steps.

• Explanation of the Code:

Tips & Variations

Common Mistakes to Avoid:

• Ignoring Edge Cases: Always consider cases like negative stairs or zero stairs.

• Inefficient Solutions: Avoid brute-force recursive solutions without memoization, as they can lead to exponential time complexity.

Alternative Ways to Answer:

• Iterative Approach: Instead of using dynamic programming, you could also implement an iterative calculation by keeping track of the last few results.

• Mathematical Approach: For specific constraints, use combinatorial mathematics to derive the answer directly.

Role-Specific Variations:

• Technical Roles: Focus on the efficiency and optimization of the algorithm.

• Managerial Positions: Emphasize your strategic approach to breaking down complex problems and leading teams in technical projects.

• Creative Roles: Showcase your unique problem-solving skills and how you might visualize or structure the solution.

Follow-Up Questions:

• What if the maximum number of steps was unlimited?

• How would your solution change if you were to optimize for space complexity?

• Can you describe a scenario where this algorithm might fail or be inefficient?

By breaking down the problem and providing a clear, structured response, candidates can demonstrate their technical skills and problem-solving abilities effectively during interviews