MCQS

Problem Preview

"Q23: A: Insert O(1), Extract O(n) B: Insert O(n), Extract O(1) C: Insert O(log n), Extract O(log n) Q24: Choose Unsorted Array (A) since insertions are constant time, making it ideal for frequent inserts. Q25: Choose Binary Heap (C) because it provides O(log n) complexity for both insertion and extraction. Q26: A sorted array is inefficient for frequent insertions because maintaining order requires shifting elements, resulting in O(n) insertion time. Q33: Waste-slot count-size full-flag costs capacity counter memory flag overhead respectively. Q34: Modulo wraps indices; removing causes out-of-bounds writes, undefined behavior after eight pushes. Q35: Revoke exposed secrets, rotate credentials, replace shared SSH keys, audit access immediately. Q36: Use a centralized secrets manager, load secrets during runtime, enforce least-privilege access, and rotate credentials periodically. Q37: Store secrets in a centralized vault, inject them at runtime, grant only necessary access, and rotate credentials regularly. Q38: Base64 encoding does not secure Kubernetes Secrets; add encryption at rest, least-privilege RBAC, audit logging, and external secret management. Q39: Represent the problem as a directed weighted graph where teams are nodes, wins are directed edges, and edge weights indicate win frequency or confidence. Q40: Major challenges include sparse comparisons, disconnected groups, and cyclic results, which reduce ranking reliability. Q41: Build the graph, merge strongly connected components (SCCs), topologically sort the condensed DAG, and resolve ties using edge weights or confidence scores. Q42: Confidence depends on the number of comparisons and the consistency of results. Limited data, conflicting outcomes, or cycles reduce confidence in the ranking."