UGC NET Computer Science Data Structures Previous Year Questions (PYQs)
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Maintaining a graph in memory by means of its adjacency matrix is known as
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Solution
- An adjacency matrix represents a graph using a 2D array (matrix).
- This type of storage uses sequential memory allocation — rows and columns are stored in contiguous memory.
- Hence, this method is called a Sequential Representation of a graph.
Which of the following trees are height balanced?
- A. Binary Search Tree
- B. AVL Tree
- C. Red-Black Tree
- D. B-Tree
Choose the correct answer from the options below:
- A and D only
- A, B and D only
- C and D only
- B and C only
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Solution
Answer: 4) B and C only
- Binary Search Tree (BST): Not necessarily balanced; height can degrade to O(n).
- AVL Tree: Height-balanced by definition (balance factor −1, 0, +1); height is O(log n).
- Red-Black Tree: Enforces black-height properties that keep height O(log n); treated as a height-balanced binary tree.
- B-Tree: Balanced (all leaves at the same level) but not a binary tree; the term “height-balanced tree” typically refers to binary trees. Hence excluded here.
When developing a dynamic programming algorithm, the sequence of steps followed is:
- A. Construct an optimal solution from computed information.
- B. Recursively define the value of an optimal solution.
- C. Characterize the structure of an optimal solution.
- D. Compute the value of an optimal solution, typically in a bottom-up fashion.
Choose the correct answer from the options given below:
- B, C, A, D
- B, A, C, D
- C, B, A, D
- C, B, D, A
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Solution
Answer: (4) C, B, D, A
Explanation:
The standard steps for designing a dynamic programming algorithm are:
- (C) Characterize the structure of an optimal solution.
- (B) Recursively define the value of an optimal solution.
- (D) Compute the value of an optimal solution (usually bottom-up).
- (A) Construct the optimal solution from the computed information.
Hence, the correct order is C → B → D → A.
The correct sequence of constructing Huffman tree is
A. Repeat until root formed
B. Create leaf nodes
C. Build priority queue
D. Combine lowest frequency nodes
Choose the correct answer from the options given below:
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Solution
Create leaf nodes (B) for each character with their frequency.
Build a priority queue (C) (min-heap) based on frequency.
Combine lowest frequency nodes (D) repeatedly to form new internal nodes.
Repeat until root formed (A) — the final remaining node is the root of the Huffman tree.
Hence, the correct order is B → C → D → A.
The longest common subsequence of $\{1,2,3,2,4,1,2\}$ and $\{2,4,3,1,2,1\}$ is
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Solution
Why: The LCS length for the two sequences is $4$. $2,3,2,1$ is a subsequence of both: $\{1,\underline{2},3,\underline{2},4,1,\underline{2}\}$ and $\{\underline{2},4,\underline{3},1,\underline{2},\underline{1}\}$. Option (4) has length $5$ (too long), and (1), (2) are not subsequences of both.
| List I | List II |
|---|---|
| A. Circular Queue | III. Dijkstra algorithm |
| B. Priority Queue | II. CPU Scheduling |
| C. Double Ended Queue | I. Print Queue |
| D. Simple Queue | IV. Palindrome checking |
Choose the correct answer from the options given below:
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Solution
Consider the following infix expression:
Q = ((A + B) * D) ↑ (E − F)
The equivalent postfix expression of Q is:
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Solution
Step-by-step conversion:
Infix: ((A + B) * D) ↑ (E − F)
(A + B) → AB+
((A + B) * D) → AB+D*
(E − F) → EF−
Combine with ↑ → AB+D*EF−↑
Match List I with List II
| List I (Hashing Collision Handling Method) | List II (Strategy) |
|---|---|
| A. Chaining | I. Check next slot |
| B. Linear Probing | II. Use second hash function |
| C. Quadratic Probing | III. Linked list at index |
| D. Double Hashing | IV. Skip slots using quadratic step |
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Solution
Chaining → Linked list at index (III)
Linear probing → Check next slot (I)
Quadratic probing → Skip slots using quadratic step (IV)
Double hashing → Use second hash function (II)
Which of the following algorithms use the Greedy strategy?
A. Dijkstra’s algorithm
B. Kruskal’s algorithm
C. Huffman coding
D. Bellman-Ford algorithm
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Solution
Dijkstra’s algorithm ✅ — Uses greedy approach to select the next minimum-distance vertex.
Kruskal’s algorithm ✅ — Uses greedy strategy to build Minimum Spanning Tree (MST).
Huffman coding ✅ — Greedy algorithm for constructing optimal prefix codes.
Bellman-Ford algorithm ❌ — Uses dynamic programming, not greedy.
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