BST Practice Question

Duration: 2 min

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Inside: a video lesson and guided study material.

Module outline

  1. English Comprehension/Verbal Ability: Comprehension / Reading Comprehension / Unseen Passages (Critical Reasoning) (Paragraph Questions), Vocabulary, Sentence Correction (Error Correction), Sentence Completion (Fill in the blanks), Sentence Re-arrangements (Para Jumbles) (Narrative Sequencing), Subject Verb Agreement (Verb Noun Agreement), Tenses, Verb, Articles, Preposition, Conjunction, Pronoun, Noun, Adjectives, Adverb, Modals, Cloze Test (Cloze Passage), Active Passive Voice (Voice), Direct and Indirect Speech, Sentence Construction
  2. Logical Ability: Coding Decoding, Directions (Direction Test), Analogy, Inequality, Data Sufficiency, Series (Number and Letter Series) (Numerical Relations and Reasoning), Syllogisms, Seating Arrangements, Puzzle Solving (Puzzles), Blood Relations, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Ranking, Order and Sequence, Logical Sequence Of Words, Data Arrangement, Venn Diagram, Calendar, Clock
  3. Quantitative Ability: Logarithm, Permutation and Combination, Probability, Profit and Loss, Speed Time and Distance, Mixture and Alligation, Partnership, Simple Interest and Compound Interest, Percentage, Ratio and Proportion (Ratios), Average, Time and Work, Pipe and Cistern, Number System, Divisibility Rules, HCF LCM, Algebra, Powers and Exponents (Surds and Indices), Mensuration and Geometry, Age Problems, Data Interpretation, Sequence and Series (Series)
  4. Basic Analytical Ability: Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning), Analytical Reasoning (Counting Figures Reasoning), Cubes & Dices, Selection Decision Table (Decision Making)
  5. Computer Programming: Pseudo Codes, Coding Questions by Yash Sir, C Programming by Yash Sir, Programming in C++, Java, Python for Placements by Yash Sir
  6. DBMS (Computer Science): Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Transaction Management, Concurrency Control, Database Security & Authorization
  7. SQL (Computer Science): SQL
  8. OS (Computer Science): Introduction to OS, Process Management, CPU Scheduling, Deadlock, Process Synchronization, Threads & Process Creation, Memory Management, Virtual Memory, File Management
  9. Computer Networks (Computer Science): 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
  10. Data Structures (Computer Science): Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  11. OOPs (Computer Science): Object Oriented Programming (OOPS)
  12. Software Engineering (Computer Science): Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management
  13. SVAR Conversational - Spoken English: Communication Assessment
  14. HR Interview: MR & HR Interview Preparation
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

This lecture segment addresses a problem involving the calculation of the expected number of comparisons for a random search operation within a Binary Search Tree (BST). The instructor presents a specific tree structure and asks students to determine the average search cost. The solution involves identifying the depth of every node, summing these depths to find the total comparisons required for all keys, and then dividing by the total number of nodes to find the mean. The final result is matched against multiple-choice options to identify the correct answer.

Chapters

  1. 0:00 – 1:48 00:00-01:48

    The video opens with the problem statement 'Consider the following Binary Search Tree' alongside a diagram. The tree has root 10, left child 5, right child 20. Node 5 has left child 4. Node 20 has left child 15 and right child 30. Node 4 has a right child 11. The question text appears: 'If we randomly search one of the keys present in above BST, what would be the expected number of comparisons?' Options (A) 2.75, (B) 2.25, (C) 2.57 are visible. The instructor begins solving by annotating the tree with comparison counts. He marks '1' next to 10, '2' next to 5 and 20, '3' next to 4, 15, and 30, and '4' next to 11. He formulates the summation equation on the right side of the screen: 1 + 2 + 2 + 3 + 3 + 3 + 4. He underlines the sum. He divides the sum by 7, writing the fraction 18/7. He performs long division to get 2.57 and circles option (C).

The video effectively teaches how to compute the average search cost in a BST. By assigning a cost equal to the node's depth plus one and averaging these costs across all nodes, the expected number of comparisons is derived. This method highlights that unbalanced trees can have higher average search costs compared to balanced ones.

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