MCSL-064 IGNOU Guess Paper 2026-27

MCSL-064 Guess Paper: Data Structures Using Python Lab (Term-End Exam Focus)

The following structured guess paper is designed to align with the official IGNOU curriculum for MCSL-064 (Data Structures Using Python Lab), covering high-weightage topics from previous years' TEE exams (2018–2023). It emphasizes practical implementation in Python, coding efficiency, and theoretical understanding.

Key Syllabus Blocks & Question Weightage

• Block-1: Data Structures Using Python Lab Manual
  • Linked Lists (Singly, Doubly, Circular): Implementation (insertion, deletion, traversal) 25-30% weightage.
  • Stacks & Queues: Python class-based implementation with time complexity analysis 20%.
  • Trees (Binary Trees, BST): Traversal (pre-order, in-order, post-order), insertion/deletion 25%.
  • Graphs (Adjacency List/Matrix): BFS/DFS implementation in Python 15%.
  • Hash Tables: Collision handling (chaining/separate hashing) and Python dictionary operations 10%.

Exam Pattern & Time Management Tips

• Question Types:
  • Programming assignments (60%): Write Python functions for data structure operations (e.g., merge two sorted linked lists, detect cycles in a graph).
  • Theoretical (30%): Short answers on time/space complexity (e.g., ‘Compare the time complexity of insertion in a BST vs. a hash table’).
  • Case Study (10%): Debug or optimize a given Python snippet for a data structure.
• Time Allocation:
  • Allocate 45 minutes for programming questions (prioritize clarity and efficiency).
  • Spend 20 minutes on theoretical questions (focus on concise answers with examples).
  • Reserve 15 minutes for reviewing code and checking syntax errors.

Frequently Asked Questions (FAQs)

• Are previous year’s exam papers available for MCSL-064?: Yes. IGNOU releases solved question papers for past TEE sessions (June/December) on the eGyanKosh portal. Focus on coding questions from 2026-27 for pattern consistency.
• How should I approach Python-based data structure questions?: Always start by analyzing the problem’s requirements (e.g., time/space constraints). Use class-based implementations for dynamic structures (e.g., `class Node` for linked lists) and include docstrings for clarity. Example:

   class Node: def __init__(self, data): self.data = data self.next = None 

Sample High-Weightage Topics from Previous Exams

• Implement a circular doubly linked list in Python with methods for insertion at head/tail and deletion.
• Write a Python function to perform level-order traversal of a binary tree using a queue.
• Compare the average-case time complexity of searching in a hash table (with chaining) vs. a balanced BST.
• Debug the following Python code for a graph traversal (BFS/DFS) and explain the error:

   graph = {0: [1, 2], 1: [2], 2: [0]} visited = set def bfs(start): queue = [start] while queue: node = queue.pop(0) # Error: IndexError visited.add(node) for neighbor in graph[node]: if neighbor not in visited: queue.append(neighbor) 

• Design a Python class for a priority queue using a min-heap and implement the `insert` and `extract_min` operations.

Note: Refer to the official lab manual for MCSL-064 (Block-1) for additional practical exercises on data structure visualizations and Python libraries (e.g., `collections.deque` for queues).