← Snowflake Interview Insights
The key insight is that doubling is just a left shift, and to maximize OR you want to concentrate all k doublings on the largest element.
First, clarify the problem constraints and edge cases. Then, explain that to maximize the bitwise OR, we should focus on setting the highest possible bits, which can be achieved by doubling the element with the highest potential to set new bits. Use a greedy strategy: repeatedly double the element that yields the greatest increase in the OR value, up to k times.
Pro tip: Mention that the greedy choice is optimal because doubling an element shifts its bits left, and setting a higher bit always dominates any combination of lower bits. Also, note that the operations are independent and can be applied to the same element multiple times.
Restate the problem in your own words: we have an array, can double any element up to k times, and want to maximize the bitwise OR of all elements. Clarify that operations can be applied to the same element multiple times.
The bitwise OR is maximized by setting the highest possible bits. Since doubling shifts bits left, we want to use operations to set the most significant bits possible.
At each step, double the element that results in the largest increase in the OR value. This can be done by simulating the process or by analyzing the binary representation of the numbers.
Argue that the greedy choice is optimal: setting a higher bit always increases the OR more than any combination of lower bits. Therefore, focusing on the highest bits is always beneficial.
Discuss time complexity (e.g., O(k * n) for naive simulation, or more efficient with priority queue) and handle edge cases like k=0, all zeros, or negative numbers (if allowed).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and edge cases, then design a clean class hierarchy with Student as a base class and Result as a derived class. Implement methods for marks entry, percentage calculation, and recheck handling, ensuring proper validation and encapsulation. Test with boundary cases like exactly 33.33% and invalid inputs.
Pro tip: Demonstrate production-level thinking by discussing how you'd extend this to handle multiple semesters, different grading schemes, or concurrency, and mention that you'd use composition over inheritance if the relationship isn't strictly 'is-a'.
Ask about input validation, recheck process (e.g., re-evaluation of marks, fee, status tracking), and whether Result should inherit from Student or contain one. Confirm pass mark logic and rounding.
Define Student with basic attributes (name, id) and methods. Make Result inherit from Student, adding marks array, percentage calculation, and recheck methods. Consider using an interface for recheckable entities.
Write constructors, getters/setters with validation (marks between 0-100), calculatePercentage() that returns a double, and isPassed() checking against 33.33%. Implement requestRecheck() to update status and possibly recalculate.
Model recheck as a state transition (e.g., PENDING, APPROVED, REJECTED). Allow marks update upon approval and recalculate percentage. Discuss how to persist or log recheck requests.
Write unit tests for boundary cases (exactly 33.33%, zero marks, invalid inputs). Discuss how to extend for more subjects, different grading systems, or integration with external systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.