Data Structures: Core Concepts: 17 MCQs with answers

Data structures

`ds` is the near-universal abbreviation for **data structures** in computer science courses. The other readings belong to statistics, machine learning, and networking respectively.

Data Science

The context signal — statistics and modelling — points to **Data Science**, not data structures. The abbreviation only means something inside its context.

Array

An **array** stores elements contiguously, so the address of index $i$ is computed directly — $O(1)$ access. Linked lists and trees require traversal.

Stack

The back button needs **last in, first out** behaviour — that is a **stack**. A queue would give first in, first out, which is wrong for history.

Tree

Directories nest inside directories, which is a hierarchical structure — a **tree**. Arrays, queues, and stacks are linear.

True

`ds` is an overloaded abbreviation: it most commonly means **data structures** in computer science and **Data Science** in statistics/analytics contexts.

Linked list

Holding the node, a **linked list** inserts in $O(1)$ by rewiring pointers. Arrays and sorted arrays need $O(n)$ shifting, and hash tables do not preserve order.

The left < node < right invariant lets each step discard half the tree

The **BST invariant** guarantees that at each comparison you can eliminate one entire subtree — halving the search space, which yields $O(\log n)$.

Many collisions mapping different keys to the same bucket

Under heavy **collisions**, lookups fall back to scanning within buckets and the average $O(1)$ degrades. The other options are not causes of degradation.

Storage layout, invariants, and operations

Every data structure is defined by how it stores data (**layout**), what must always hold (**invariants**), and what it can do (**operations**).

The wrong data structure was chosen for the workload

Choosing the wrong structure is a common cause of slow software. The complexity class of the structure, not the language or hardware, is what turns milliseconds into seconds.

Cleaning data and framing the question

Cleaning and framing the question dominate the workflow. Deciding what to measure and whether the measurement is trustworthy is the hard part, not the modelling.

An abbreviation is a label whose meaning depends on context, so the definition is a guess

`ds` is overloaded — it also means Data Science, Decision Stump, Dataset, and distribution system. Treating the label as a definition skips the disambiguation step.

A graph with shortest-path algorithms over a priority queue

Route finding models places as **graph** nodes and roads as edges, then runs shortest-path algorithms using a priority queue. A stack or hash map alone cannot express the road network.

Both A and R are true, and R is the correct explanation of A.

The trade-off in R is exactly why A holds: arrays win on access, linked lists on insertion, hash tables on average lookup — so the right choice depends on the workload.

I and II only

Statements I and II are both true readings of `ds`. Statement III is false — the abbreviation is overloaded, and its meaning depends on the field, course, and chapter.

Linked List

The correct option is a Linked List because it allows for $O(1)$ insertions and deletions once you have a reference to the node, making it ideal for dynamic lists where elements are frequently added or removed. In contrast, an Array or Sorted Array would require $O(n)$ time for these operations, and a Hash Table does n…