Question
When should a Python developer choose heapq and Priority Queues deliberately?
- Use heapq for top-k problems, scheduling by priority, or repeatedly taking the current smallest or largest candidate.
- Choose heapq and Priority Queues mainly when you want to postpone validation and fix issues manually later.
- Choose heapq and Priority Queues whenever you want the code to look more advanced, even if the design gets less clear.
- Choose heapq and Priority Queues only to avoid modeling the real data shape or domain contract explicitly.
Hint
Think about the production scenario where the choice genuinely improves the code.
Answer and rationale
Correct answer: A. Use heapq for top-k problems, scheduling by priority, or repeatedly taking the current smallest or largest candidate.
Use heapq for top-k problems, scheduling by priority, or repeatedly taking the current smallest or largest candidate. Interviewers often ask this to see whether you can connect the concept to real design decisions.
Track: Python