Binary Tree Practice question

Duration: 7 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.

This educational video provides a detailed walkthrough of a recursive C function designed to calculate the maximum depth or height of a binary tree. The instructor begins by presenting the code structure, highlighting the base case where a NULL node returns 0. He then uses a hand-drawn diagram of a binary tree to trace the execution flow, demonstrating how the function recursively calls itself for left and right subtrees. The core of the lesson focuses on determining the correct return values for the recursive step, specifically explaining why the depth of a node is calculated as one plus the maximum depth of its children. The instructor systematically eliminates incorrect options to arrive at the correct implementation.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The instructor introduces the problem of finding the maximum depth of a binary tree, where the root is at level 1. He displays the C code for the function `maxDepth(struct node* node)` and points out the base case condition `if (node==NULL) return 0;`. To visualize the recursion, he draws a simple binary tree structure on the screen with a root node labeled 'A' and two children labeled 'B' and 'C'. He explains that the function needs to compute the depth of each subtree recursively.

  2. 2:00 – 5:00 02:00-05:00

    The instructor expands the tree diagram by adding a node 'D' as a child of 'B' to create a deeper structure. He traces the execution of `maxDepth(A)`, which calls `maxDepth(B)` and `maxDepth(C)`. He explains that `lDepth` stores the result of the left subtree call and `rDepth` stores the result of the right subtree call. He specifically traces the call for node 'D', noting that since its children are NULL, the function returns 0 for those calls. He then calculates the depth for node 'D' itself as 1, and subsequently for node 'B', showing how the values propagate back up the call stack.

  3. 5:00 – 6:55 05:00-06:55

    Focusing on the final logic, the instructor analyzes the lines `if (lDepth > rDepth) return X; else return Y;`. He deduces that to correctly calculate the height, the function must return the depth of the current node, which is 1 plus the depth of the deeper subtree. Therefore, he concludes that X must be `lDepth + 1` and Y must be `rDepth + 1`. He identifies option (B) as the correct answer, explaining that simply returning `lDepth` or `rDepth` without adding 1 would fail to account for the current node's level in the tree.

The video effectively demonstrates the recursive logic required for tree traversal problems. By breaking down the problem into a base case and a recursive step, and using a visual diagram to trace the execution, the instructor clarifies how the height is accumulated. The key takeaway is that the height of a node is defined as one plus the maximum height of its children, a fundamental concept in tree algorithms.

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