In a dynamic array (such as Python's list or Java's ArrayList) that doubles its capacity whenever it becomes full, what is the amortized time complexity of a single append operation?

Although any single append that triggers a resize costs O(n) time (to copy all existing elements into the newly allocated, larger array), doubling the capacity each time means resizes become exponentially less frequent as the array grows — the total cost of all resizes across n appends sums to O(n), which, when averaged (amortized) across all n individual append operations, works out to O(1) per append. This is why dynamic arrays are described as having O(1) amortized append time, not O(1) strict worst-case time (option E is incorrect, since an individual resizing append genuinely does cost O(n) in that specific instance) — the O(1) claim only holds when averaged over a sequence of operations.