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    GATE DA Subject Wise Weightage 2026 - AI Topic Wise Marks Distribution

    GATE DA Subject Wise Weightage 2026 - AI Topic Wise Marks Distribution

    Team Careers360Updated on 26 Nov 2025, 09:32 AM IST

    GATE DA Subject Wise Weightage 2026: IIT Guwahati will conduct the GATE 2026 exam online. IIT Guwahati will post the test pattern and curriculum for GATE Data Science and Artificial Intelligence on its official website. The GATE exam 2026 will be held for 3 hours for each paper. The exam pattern for GATE 2026 is updated on this page. Here in this article, we know about GATE DA subject wise weightage 2026. The GATE 2026 exam is going to happen on February 7, 8, 14 and 15, 2026 . Candidates studying for the GATE exam should see the GATE DA Question Papers to understand the topics that will be assessed on the exam. Understanding the Topic, GATE DA subject wise weightage is the first and most important step in preparing for the GATE exam. Let's dive into the DA weightage and get you prepared for GATE 2026 exam.

    GATE DA Subject Wise Weightage 2026 - AI Topic Wise Marks Distribution
    GATE DA Subject Wise Weightage 2026- AI Topic Wise Marks Distribution

    GATE DA and AI Subject Wise Weightage 2026

    The introduction of the Data Science and Artificial Intelligence (DA) paper in GATE 2024 marks a significant stride in India's technological advancement. GATE has aligned itself with the nation's growing digital landscape by providing a platform for aspiring data scientists and AI engineers to showcase their expertise. This move is expected to fuel innovation and research in these critical fields:

    Chapter Name20252024Total

    Percentage Distribution

    Aptitude10102015.38%
    Artificial Intelligence (AI)47118.46%
    Calculus and Optimization65118.46%
    Database Management and Warehousing74118.46%
    Linear Algebra861410.77%
    Machine Learning9101914.62%
    Probability and Statistics12102216.92%
    Programming, Data Structures and Algorithms9132216.92%
    Total6565130100.00%
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    A comprehensive view of the the weightage is given in the chart below to help you visualize the weightage better:

    1755103348602

    What is the Most Important Topics in GATE DA 2026?

    The GATE Data Science and Artificial Intelligence (DA) test is divided into two parts: general aptitude and core data science and artificial intelligence courses. The weightage of General Aptitude and core Data Science and Artificial Intelligence is 15%. And 85% respectively. For a wrong answer chosen in an MCQ, there will be a negative marking. For a 1-mark MCQ, a 1/3 mark will be deducted for a wrong answer. For a 2-mark MCQ, a 2/3 mark will be deducted for a wrong answer. There is no negative marking for wrong answer(s) to MSQ or NAT questions. There is no partial marking in MSQ. Candidates can use GATE DA topic wise weightage pdf to prepare for GATE 2026.

    Chapter NameTopic Name (Specific Subtopic)Count
    AptitudeDice folding and visualization2

    Geometry – cross-section visualization2

    Graph coloring (minimum colors)2

    Inference from passage2

    Infinite series sum2

    Permutations – divisibility1

    Permutations – Divisibility rule1

    Pie chart – percentage calculation2

    Probability of combinations (girls/boys)1

    Profit/Interest calculation (returns)2

    Verbal analogy2
    Aptitude Total
    19
    Artificial IntelligenceAI – Adversarial search (alpha-beta pruning)1

    AI – Heuristic admissibility (h1, h2)1

    AI – Search strategy (A*) and heuristic admissibility1

    Alpha-beta pruning in adversarial search1

    Bayesian network – conditional independence1

    Bayesian network – joint probability computation1

    BFS vs DFS – state expansion count1

    BFS vs DFS – state space expansion1

    Logic representation – rugby and round balls1

    Neural network – weight equivalence1

    Propositional logic – tautology identification1

    Artificial Intelligence Total


    11
    Calculus and OptimizationFunction continuity and differentiability (piecewise)1

    Limits and logarithmic expansion1

    Limits and logarithmic expansions1

    Local maxima/minima (quartic polynomial)1

    Local maxima/minima of quartic polynomial1

    Logistic function derivative (0.4 value)1

    Optimization – function continuity and differentiability1

    Optimization – local minima (2nd derivative test)2

    Optimization – Taylor series and limits1

    Calculus and Optimization Total


    10
    Database Management and WarehousingER model – relational schema (DB constraints)1

    Functional dependencies (DB)1

    Functional dependencies (derivable attributes)1

    Normalization & z-score1

    Relational algebra – ensuring team members in defender/forward1

    Relational algebra – set operations (Team/Defender)1

    SQL – Index optimization (hash vs B+)1

    SQL indexing optimization (hash vs B+)1

    SQL query result count (joins with conditions)1

    Database Management and Warehousing Total


    9
    Linear AlgebraDeterminant of M2+12MM^2+12M1

    Eigenvalues and matrix properties1

    Eigenvalues and signs of matrix1

    Eigenvalues of matrices1

    Eigenvalues, determinant and matrix property1

    Matrix rank and nullity (subspaces)1

    Matrix solution scenarios (unique/infinite/none)1

    Matrix solutions (unique/infinite/no solutions)1

    Projection matrix properties2

    Python recursion & tree traversal1

    Singular values and sum1

    Singular values and their sum (SVD)1

    Subspaces of R3R^31

    Subspaces of R3R^3R31

    Vector subspace properties1
    Linear Algebra Total
    16
    Machine LearningClustering – single linkage algorithm2

    Decision tree – Information gain (entropy)2

    Fisher Linear Discriminant (between/within scatter matrices)1

    k-means clustering – point assignment1

    k-means clustering properties2

    k-NN classifier (minimum k for classification)1

    ML – Linear separability (2D datasets)1

    ML – Linear separability of datasets3

    Naive Bayes – number of parameters estimation1

    Neural network – weight equivalence (ReLU)1

    PCA, Naive Bayes, Logistic regression (classification of models)1

    SVM – support vectors1

    Machine Learning Total


    17
    Probability and StatisticsBinary search recurrence relation1

    Covariance between random variables1

    Dynamic programming (prefix computation)1

    Expected throws until two consecutive even outcomes1

    Logic – Propositional representation (balls/rugby)1

    Poisson distribution & Normal distribution properties2

    Probability – Bayes theorem2

    Probability – conditional expectation and variance1

    Probability – conditional/joint events3

    Probability – event intersection (T ∩ S)1

    Probability – expected value (die throws)1

    Probability – exponential distribution parameter2

    Probability – joint PDF and expectation2

    Probability – uniform distribution (X,Y)1

    Probability – uniform distributions1

    Probability – z-score normalization1

    Probability of combinations (girls/boys)1

    Python list reverse (recursion)1

    Sample mean update with new data1

    Sorting algorithms – bubble/insertion/selection passes1

    Probability and Statistics Total


    26
    Programming, Data Structures and AlgorithmsAI – Heuristic admissibility (h1, h2)1

    Array prefix computation (dynamic programming)1

    Bayesian network joint probability1

    Binary search comparisons recurrence1

    Binary search complexity analysis1

    Binary tree node relationships (height, leaves)1

    Binary tree properties (height, nodes)1

    Covariance between random variables1

    DFS edge classification (tree/cross/back)2

    Double-ended queue operations (insert/remove)1

    k-NN classifier (minimum k for classification)1

    Python list reverse using recursion1

    Python recursion – counting tree nodes1

    Quicksort – swaps count1

    Relational algebra – SQL tuple verification1

    Sorting algorithms – bubble/insertion/selection passes1

    Stack vs queue vs hash table (matching)1

    Topological sort of DAG1

    Topological sorting in DAG1

    Tree traversal combinations (preorder/inorder/postorder)1

    Uniform hashing – expected probes1

    Programming, Data Structures and Algorithms Total


    22
    Grand Total
    130
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    Benefits of GATE DA exam:

    A GATE DA qualification opens doors to a wide range of exciting career opportunities:

    • Higher Studies: M.Tech/M.S. in Data Science or related fields: Gain specialized knowledge and skills.

    • Ph.D. programs: Pursue research and contribute to the advancement of data science and AI.

    • Public Sector Undertakings (PSUs): Work on data-driven projects and contribute to policy-making.

    • Enjoy competitive salaries and job security.

    • Private Sector: Data Scientist: Extract valuable insights from data to drive business decisions.

    • Machine Learning Engineer: Develop and implement machine learning models.

    • AI Researcher: Conduct research to push the boundaries of AI technology.

    • IT Consultant: Provide data-driven solutions to organizations.

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    Frequently Asked Questions (FAQs)

    Q: What are the total marks for GATE DA?
    A:

    The DA exam consists of 65 questions covering a weightage of 85 marks.

    Q: What are the types of questions asked in GATE DA?
    A:

    The type of questions asked in GATE DA are 

    a) Multiple Choice Questions (MCQ)

    (b) Multiple Select Questions (MSQ) and/or

    (c) Numerical Answer Type (NAT) Questions

    Q: Which Topic has High Weightage in GATE DA?
    A:

    Probability and statistics have a high weightage topic in GATE DA.

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    Questions related to GATE

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    Have a question related to GATE ?

    Hello,

    You can find information about Mtech CS admission for 2026 without GATE qualification marks through the link provided below.

    https://engineering.careers360.com/articles/list-of-mtech-colleges-without-gate-score

    Any BTech ECE graduate can apply for all the NITs offering MTech CSE based on GATE exam. Some mid and lower NITs are:

    • NIT Warangal
    • MNIT Jaipur
    • VNIT Nagpur
    • NIT Durgapur

    You can check the complete list of NITs offers MTech CSE are - https://engineering.careers360.com/colleges/list-of-top-nit-colleges-in-india

    With a score of 423 out of 1000, candidates can expect MTech admission options mostly in mid-tier IITs, NITs, and other private institutes offering Biomedical Engineering.

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