2019(2)

Duration: 3 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 presents a multiple-choice question from the GATE 2019 exam about Dijkstra's algorithm. The instructor analyzes each of the four options to determine which statement is not true. He confirms that options (A), (B), and (D) are true: Dijkstra's algorithm can find the shortest path within the same graph data structure, it always selects the node with the smallest known distance, and it requires non-negative edge weights. He then focuses on option (C), which states that the shortest path always passes through the least number of vertices. To disprove this, he draws a graph with vertices S, A, B, C, and D, and demonstrates that the shortest path from S to D (S->A->B->D with a total cost of 8) is not the path with the fewest vertices (S->C->D with a cost of 9). This counterexample shows that the shortest path is based on minimizing total weight, not the number of vertices, making (C) the correct answer to the question.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video begins with a question displayed on screen: 'When using Dijkstra's algorithm to find shortest path in a graph, which of the following statement is not true? (NET 2019 DEC)'. The four options are listed: (A) It can find shortest path within the same graph data structure, (B) Every time a new node is visited, we choose the node with smallest known distance/cost to visit first, (C) Shortest path always passes through least number of vertices, and (D) The graph needs to have a non-negative weight on every edge. The instructor, visible in the bottom right, begins to analyze the options. He marks (A) as true, explaining that the algorithm operates on the graph itself. He marks (B) as true, stating that the core of Dijkstra's algorithm is to always pick the node with the minimum distance. He then marks (D) as true, noting that negative weights can cause the algorithm to fail. He then focuses on option (C), which he identifies as the incorrect statement, and begins to explain why.

  2. 2:00 – 2:38 02:00-02:38

    The instructor draws a graph on the screen to provide a counterexample for option (C). The graph has vertices S, A, B, C, and D. The edges are S->A (weight 1), A->B (weight 1), B->D (weight 6), S->C (weight 2), and C->D (weight 7). He calculates the cost of the path S->A->B->D as 1+1+6=8. He calculates the cost of the path S->C->D as 2+7=9. He concludes that the shortest path from S to D is S->A->B->D, which has 3 vertices (A, B, D). The path S->C->D has only 2 vertices (C, D), which is fewer. Since the shortest path does not pass through the least number of vertices, the statement in (C) is false. He marks (C) as 'false' and confirms it is the correct answer to the question.

The video systematically evaluates four statements about Dijkstra's algorithm. It confirms that the algorithm operates on the same graph, always selects the node with the minimum known distance, and requires non-negative edge weights. The key insight is that the algorithm's goal is to minimize the total path weight, not the number of vertices. The instructor uses a clear counterexample to demonstrate that a path with a lower total weight can have more vertices than a path with a higher total weight, thereby proving that the shortest path does not necessarily pass through the least number of vertices. This logical disproof identifies (C) as the only false statement.

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