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A complete breakdown of the GATE 2026 Data Science & Artificial Intelligence (DA) previous year paper — marks distribution, important topics, difficulty, and a question-by-question map of all 65 questions. Conducted by IIT Guwahati on 15 February 2026.
GATE DA 2026 was a 3-hour Computer-Based Test with 65 questions for 100 marks, split into a common General Aptitude (GA) section worth 15 marks and a core Data Science & AI section worth 85 marks. The paper mixed MCQ, MSQ (multiple-select) and NAT (numerical answer type) questions.
Negative marking applies to MCQs only: −1/3 for a 1-mark MCQ and −2/3 for a 2-mark MCQ. MSQ and NAT questions carry no negative marking.
Here is the official subject-wise distribution of all 100 marks, split by 1-mark and 2-mark questions. The bars below are scaled to the highest-weighted subject so you can see priorities at a glance.
Note: weightage follows Piyush Wairale's official subject classification. Optimization-based questions (gradient descent, regularization objective, series) are grouped under Calculus & Optimization. The question-by-question table further below is an indicative topic reference — a few boundary questions may be grouped differently there than in this headline count.
Piyush Wairale walks through the GATE DA 2026 question paper, subject-wise weightage, and the topics that mattered most — in this detailed video breakdown.
A high-value math area where questions leaned on distribution properties and clean conceptual reasoning rather than heavy calculation.
Must-know topics: Normal, t, Chi-square, Exponential (memoryless property), Bernoulli, Poisson & Uniform distributions; Bayes theorem; CDF properties; variance, covariance & correlation; counting/combinatorics.
Representative questions: Q19, Q20, Q28, Q33, Q34, Q44, Q45, Q53, Q54, Q57, Q62, Q63, Q64.
The highest-weighted subject in the paper (18 marks) — and one that rewards hands-on query practice. Two separate B+ Tree questions plus deep SQL made this a make-or-break area.
Must-know topics: B+ Trees (order, inserts, node pointers), SQL (correlated subqueries, GROUP BY, aggregates), relational algebra & tuple relational calculus, functional dependencies & candidate keys, normalization (3NF), ER modeling, OLAP (roll-up, drill-down, cuboids).
Representative questions: Q17, Q18, Q26, Q32, Q41, Q42, Q43, Q51, Q59, Q60, Q61.
Heavily application-based. Expect to trace Python code and reason about algorithm behaviour rather than recall definitions.
Must-know topics: Sorting (quicksort recurrence, bubble vs. insertion sort), binary search, binary tree traversals, graph BFS/DFS & reachability, recursion & the runtime stack, Python gotchas (mutable default arguments, closures).
Representative questions: Q15, Q16, Q25, Q31, Q39, Q40, Q49, Q50, Q58.
Numerical and definitional in equal measure. Several questions asked you to compute a metric or count parameters.
Must-know topics: PCA, cross-validation (LOOCV), clustering (hierarchical, K-medoid), regression & regularization (Ridge / L1 vs L2), classification metrics (accuracy, precision, recall), gradient descent (SGD), MLP parameter counting, bias–variance tradeoff.
Representative questions: Q11, Q12, Q23, Q29, Q36, Q37, Q47, Q55, Q56.
A reliable, concept-driven scorer. The projection matrix M = I − (1/n)11ᵀ appeared twice (Q52 & Q65).
Must-know topics: eigenvalues, rotation matrices & matrix powers, projection & idempotent matrices, quadratic forms (max xᵀAx), vector spaces & subspaces.
Representative questions: Q21, Q22, Q46, Q52, Q65.
Logic-heavy this year. First-order logic showed up in multiple questions, so precision with quantifiers pays off.
Must-know topics: informed vs. uninformed search (A*, BFS, DFS), first-order / predicate logic & validity, logical entailment & equivalence, minimax & game trees.
Representative questions: Q13, Q14, Q24, Q30, Q38, Q48.
Bigger than it looks once optimization is counted here. Gradient descent, regularization objectives and series questions all live in this bucket — a genuinely high-yield area, not an afterthought.
Must-know topics: maxima/minima & roots of functions, gradient descent / stochastic gradient descent, regularization (ridge) objectives, sequences & series (geometric series summation), convergence.
Representative questions: Q27, Q29, Q35 (plus optimization steps embedded in Q37 & Q55).
Every one of the 65 questions mapped to its subject and topic. Use this to spot exactly which concepts were tested.
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DBMS & Data Warehousing had the highest weightage with 18 marks. PDSA and Machine Learning followed with 16 marks each.
There were 65 questions for 100 marks: 10 General Aptitude questions (15 marks) and 55 core Data Science & AI questions (85 marks). Q1–Q5 and Q11–Q35 carried 1 mark each; Q6–Q10 and Q36–Q65 carried 2 marks each.
DBMS & Data Warehousing 18, PDSA 16, Machine Learning 16, General Aptitude 15, Probability & Statistics 11, Calculus & Optimization 9, Linear Algebra 8, and Artificial Intelligence 8 — totalling 100 marks.
Overall moderate to moderately difficult. Programming and DBMS were application-heavy (Python closures, correlated SQL subqueries, B+ Tree inserts), while Probability & Statistics rewarded conceptual clarity over rote formulas.
Detailed solutions, topic-wise practice, and full-length mock tests for GATE DA are available in Piyush Wairale's GATE DA 2027 course and test series.
Analysis based on the official GATE 2026 Data Science & AI question paper (IIT Guwahati). © PiyushAI Edtech. Subject classification reflects the official GATE DA syllabus; boundary questions are noted where relevant.