Tree Practice Question

Duration: 2 min

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Inside: a video lesson and guided study material.

Module outline

  1. Quantitative Ability: Percentage, Profit and Loss, Ratio and Proportion (Ratios), Average, Time and Work, Speed Time and Distance, Simple Interest and Compound Interest, Age Problems, Number System, Divisibility Rules, HCF LCM, Algebra, Permutation and Combination, Probability, Data Interpretation, Logarithm, Sequence and Series (Series), Powers and Exponents (Surds and Indices), Speed Mathematics and Basic Mathematics
  2. Logical Reasoning: Seating Arrangements, Puzzle Solving (Puzzles), Data Arrangement, Coding Decoding, Blood Relations, Directions (Direction Test), Ranking, Order and Sequence, Data Sufficiency, Venn Diagram, Inequality, Series (Number and Letter Series) (Numerical Relations and Reasoning), Logical Sequence Of Words, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Selection Decision Table (Decision Making), Analogy, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning)
  3. Verbal Ability: Comprehension / Reading Comprehension / Unseen Passages (Critical Reasoning) (Paragraph Questions), Subject Verb Agreement (Verb Noun Agreement), Tenses, Articles, Preposition, Conjunction, Modals, Active Passive Voice (Voice), Direct and Indirect Speech, Vocabulary, Sentence Re-arrangements (Para Jumbles) (Narrative Sequencing), Sentence Completion (Fill in the blanks), Sentence Correction (Error Correction), Cloze Test (Cloze Passage), Facts, Inference and Judgements
  4. Programming Fundamentals (Technical): C Fundamentals
  5. Data Structures (Technical): Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  6. Algorithms (Technical): Algorithm Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Time Complexity Analysis, Advanced Algorithms
  7. OOPs (Technical): Object Oriented Programming (OOPS)
  8. DBMS (Technical): Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, Relational Calculus, Transaction Management, Concurrency Control, Database Recovery, ORDBMS, Database Security & Authorization, Query Processing & Optimization, Enhanced Data Models, Data Warehousing & Mining, Big Data Systems, NoSQL
  9. SQL (Technical): SQL
  10. Operating Systems (Technical): Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Deadlock, Threads & Process Creation, Memory Management, Virtual Memory, Disc Scheduling, File Management, Windows OS, Linux OS, Security, Distributed Systems, Virtual Machines
  11. Computer Networks (Technical): Introduction to CN, Data Communication, DLL: Access Control, DLL: Flow Control, DLL: Error Control, DLL: Framing, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Net Layer:Routing Protocol, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware basics, Network Security, Mobile Technology, Cloud Computing and IoT, Cyber and Info Security
  12. Software Engineering (Technical): Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management
  13. Coding/Automata Round: Coding Questions by Yash Sir, C Programming by Yash Sir, Programming in C++, Java, Python for Placements by Yash Sir, Pseudo Codes
  14. Personality / Behavioral Assessment: Behavioral Assessment (Behavioral Competency Profiling) (Psychometric Test)
  15. Writing Assessment: English Writing Ability
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video features an educational lecture by Sanchit Jain Sir from Knowledge Gate, focusing on a C programming problem involving binary trees. The specific task is to complete a function named `maxDepth` which calculates the maximum depth or height of a binary tree, noting that the root is at level 1. The code snippet provided on screen defines the function signature `int maxDepth(struct node* node)`. The instructor walks through the logic, starting with the base case: `if (node == NULL) return 0;`. This condition ensures that if a node does not exist, the depth contribution is zero. Following the base case, the `else` block handles the recursive step. The code calculates the depth of the left subtree using `int lDepth = maxDepth(node->left);` and the right subtree using `int rDepth = maxDepth(node->right);`. The critical part of the lecture is analyzing the final return statements. The code presents a conditional check: `if (lDepth > rDepth) return X; else return Y;`. The instructor explains that to find the total height, one must take the larger of the two subtree depths and add 1 to account for the current node itself. Consequently, the placeholders X and Y must represent the depth of the respective subtrees incremented by one. The question text at the bottom asks: "What should be the values of X and Y so that the function works correctly?". The visible options are (A) X = lDepth, Y = rDepth; (B) X = lDepth + 1, Y = rDepth + 1; (C) X = lDepth - 1, Y = rDepth - 1; and (D) None of the above. The instructor emphasizes that since the depth includes the current node, the value returned must be the subtree depth plus one. He points to the code structure to justify why adding 1 is necessary for the correct calculation of tree height, highlighting the recursive definition where height is 1 + max(left_height, right_height). The watermark "KNOWLEDGEGATE" is visible throughout the screen. The instructor is seated in a black gaming chair, wearing a white t-shirt with the Knowledge Gate logo.

Chapters

  1. 0:00 – 1:39 00:00-01:39

    The video analyzes a C function `maxDepth` for binary trees. It covers the base case `if (node==NULL) return 0;`, the recursive calculation of `lDepth` and `rDepth`, and the logic to determine the return values X and Y, which must account for the current node's level by adding 1 to the subtree depths. The instructor explains that the height of a node is 1 plus the maximum height of its children, requiring the addition of 1 in the return statement.

The lecture reinforces the recursive definition of tree height, where the height of a node is 1 plus the maximum height of its children, requiring the addition of 1 in the return statement to correctly calculate the total depth.

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