Using namespace std;

What this Python performance comparison question tests

This is a medium-difficulty question that probes your practical understanding of how CPython resolves names and executes bytecode. Rather than testing algorithmic thinking, it rewards knowledge of Python's runtime behaviour—specifically, how namespace lookups and function call overhead vary depending on scope and binding strategy.

To compare the runtime of different function implementations, you need to reason about the cost of name resolution in CPython's execution model. Local variables are faster to access than module-level or global names; similarly, how you bind functions and methods affects dispatch overhead. This question is typical of interviews at firms that care about optimizing compute-heavy Python workloads, and it reflects real-world concerns covered in texts like High Performance Python.

• CPython bytecode and the LOAD_* instruction family
• Local vs. global namespace lookup costs
• Function call overhead and method binding
• Micro-optimization patterns in tight loops