GATE DA Knowledge Representation and Reasoning: 2 chapters, 7 previous year questions (50% of Artificial Intelligence), 0 practice questions and one solved qu
Creating an account is free. You get the rest of this chapter, step-by-step solutions, and a study plan built around the topics you are actually weak at.
Most platforms hand everyone the same content. Here the content moves with your performance, topic by topic.
Every answer you give moves your topic-level intelligence rate. The next question, the next revision card and tomorrow's plan all change with it.
We only revise topics you have actually attempted and are still below the safe bar on — never the same chapter on repeat.
Each question carries a measured toughness. You are served a rung above your current level, so practice keeps stretching you.
Full lesson cards for first study, curated short-note cards for the last mile — with derivations, traps and exam patterns marked.
Notes, chapter practice, previous-year questions, test series and full-length papers — all feeding one picture of your preparation.
No vanity streaks. Progress here means chapters mastered and accuracy that held up on harder questions.
Full notes and short notes, the complete question bank with worked solutions, mock tests, full-length papers, and an adaptive plan that rebuilds itself as you improve.
GATE DA Knowledge Representation and Reasoning: 2 chapters, 7 previous year questions (50% of Artificial Intelligence), 0 practice questions and one solved question from each chapter.
7 previous year questions from Knowledge Representation and Reasoning in GATE DA, grouped by chapter with the exam year, answer key and step-by-step solution for each.
Knowledge Representation and Reasoning accounts for 7 of 14 Artificial Intelligence previous year questions in our bank (50%), about 2.3 per paper across 3 papers.
| Chapter | Topics | PYQs | Share of unit PYQs | Practice questions |
|---|---|---|---|---|
| Logic, First-Order Representation and Entailment | First-Order Logic Translation and Quantifier Validity, Logical Entailment, Implication and Predicate Reasoning | 4 | 57% | 0 |
| Bayesian Networks and Probabilistic Inference | Bayesian Network Inference Algorithms and CPT Computation, Bayesian Network Factorization and Conditional Independence | 3 | 43% | 0 |
Which of the following statements is/are correct in a Bayesian network?