GATE Exam Date:07 Feb' 26 - 08 Feb' 26
Most Scoring Topics in DA for GATE 2026 - The GATE Data Science and Artificial Intelligence (DA) 2026 paper is specially designed for candidates aiming for higher studies or careers in AI, machine learning, and data-driven technologies. This paper, introduced in 2024, is also part of the GATE 2026 exam to be conducted by IIT Guwahati and is scheduled for around February 2026. For effective preparation, candidates are looking for the important topics as per past years' DA papers. Their search ends here, as this article provides the most scoring topics from previous years, along with the question count and the exam pattern. This will help in better preparation of GATE 2026 exam. To know more, read the full article.
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Understanding the exam pattern of GATE 2026 helps to know more about the exam. Let's understand the pattern via the table given below:
Feature | Details |
Exam Mode | Computer-Based Test (CBT) |
Duration | 3 Hours |
Total Questions | 65 |
Total Marks | 100 |
Question Types | MCQ (Multiple Choice), MSQ (Multiple Select), NAT (Numerical Answer) |
Sections | General Aptitude (15%), Engineering Mathematics (CSE) / Maths (DA), Core Subject |
Marking Scheme | 1 or 2 marks per question; negative marking for MCQs only |
As we know, this paper was introduced in 2024. Based on the previous year’s analysis, the high-scoring topics are mentioned in the table below.
Chapter Name | Percentage Distribution |
Aptitude | 15.38% |
Artificial Intelligence (AI) | 8.46% |
Calculus and Optimization | 8.46% |
Database Management and Warehousing | 8.46% |
Linear Algebra | 10.77% |
Machine Learning | 14.62% |
Probability and Statistics | 16.92% |
Programming, Data Structures and Algorithms | 16.92% |
Total | 100.00% |
As per the table, Probability and Statistics seem to have the highest weightage along with Programming and Algorithms. Well, this was the overall chapter-wise weightage and now it's time to dive into GATE DA 2025 important topics along with GATE DA 2024 important topics. Mastering the most scoring topics in DA for GATE 2026 ensures better accuracy and faster problem-solving. Since only 2 years have this distribution, we will look at the high scoring topics in the DA for GATE 2026
Also Read: GATE 2026 Syllabus for Data Science & Artificial Intelligence (DA)
Knowing the most scoring topics in DA for GATE 2026 allows you to prioritize high-yield concepts during revision. Please Note: Exams have taken place only twice, therefore to get an understanding of the high-scoring topics in DA for GATE 2026, we have combined the GATE DA 2025 and 2024 important topics and chapters in the table given below:
Chapter Name | Topic Name (Specific Subtopic) | Question Count |
Aptitude | Dice folding and visualization | 2 |
Geometry – cross-section visualization | 2 | |
Graph coloring (minimum colors) | 2 | |
Inference from the passage | 2 | |
Infinite series sum | 2 | |
Permutations – Divisibility rule | 2 | |
Pie chart – percentage calculation | 2 | |
Probability of combinations (girls/boys) | 2 | |
Profit/Interest calculation (returns) | 2 | |
Verbal analogy | 2 | |
Aptitude Total | 20 | |
Artificial Intelligence | AI – Adversarial search (alpha-beta pruning) | 1 |
AI – Heuristic admissibility (h1, h2) | 1 | |
AI – Search strategy (A*) and heuristic admissibility | 1 | |
Alpha-beta pruning in adversarial search | 1 | |
Bayesian network – conditional independence | 1 | |
Bayesian network – joint probability computation | 1 | |
BFS vs DFS – state expansion count | 1 | |
BFS vs DFS – state space expansion | 1 | |
Logic representation – rugby and round balls | 1 | |
Neural network – weight equivalence | 1 | |
Propositional logic – tautology identification | 1 | |
Artificial Intelligence Total | 11 | |
Calculus and Optimization | Function continuity and differentiability (piecewise) | 1 |
Limits and logarithmic expansion | 1 | |
Local maxima/minima (quartic polynomial) | 2 | |
Logistic function derivative (0.4 value) | 1 | |
Optimization – function continuity and differentiability | 2 | |
Optimization – local minima (2nd derivative test) | 2 | |
Optimization – Taylor series and limits | 1 | |
Calculus and Optimization Total | 10 | |
Database Management and Warehousing | ER model – relational schema (DB constraints) | 1 |
Functional dependencies (derivable attributes) | 2 | |
Normalization & z-score | 1 | |
Relational algebra – ensuring team members in defender/forward | 1 | |
Relational algebra – set operations (Team/Defender) | 1 | |
SQL – Index optimization (hash vs B+) | 2 | |
SQL query result count (joins with conditions) | 1 | |
Database Management and Warehousing Total | 9 | |
Linear Algebra | Determinant of M2+12M | 1 |
Eigenvalues and matrix properties | 1 | |
Eigenvalues and signs of matrix | 1 | |
Eigenvalues of matrices | 1 | |
Eigenvalues, determinant and matrix property | 1 | |
Matrix rank and nullity (subspaces) | 1 | |
Matrix solution scenarios (unique/infinite/none) | 2 | |
Projection matrix properties | 2 | |
Python recursion & tree traversal | 1 | |
Singular values and their sum (SVD) | 2 | |
Subspaces of R3 | 1 | |
Vector subspace properties | 1 | |
Linear Algebra Total | 15 | |
Machine Learning | Clustering – single linkage algorithm | 2 |
Decision tree – Information gain (entropy) | 2 | |
Fisher Linear Discriminant (between/within scatter matrices) | 1 | |
k-means clustering – point assignment | 1 | |
k-means clustering properties | 2 | |
k-NN classifier (minimum k for classification) | 1 | |
ML – Linear separability of datasets | 4 | |
Naive Bayes – number of parameters estimation | 1 | |
Neural network – weight equivalence (ReLU) | 1 | |
PCA, Naive Bayes, Logistic regression (classification of models) | 1 | |
SVM – support vectors | 1 | |
Machine Learning Total | 17 | |
Probability and Statistics | Binary search recurrence relation | 1 |
Covariance between random variables | 1 | |
Dynamic programming (prefix computation) | 1 | |
Expected throws until two consecutive even outcomes | 1 | |
Logic – Propositional representation (balls/rugby) | 1 | |
Poisson distribution & Normal distribution properties | 2 | |
Probability – Bayes theorem | 2 | |
Probability – conditional expectation and variance | 1 | |
Probability – conditional/joint events | 3 | |
Probability – event intersection (T ∩ S) | 1 | |
Probability – expected value (die throws) | 1 | |
Probability – exponential distribution parameter | 2 | |
Probability – joint PDF and expectation | 2 | |
Probability – uniform distribution (X,Y) | 1 | |
Probability – uniform distributions | 1 | |
Probability – z-score normalization | 1 | |
Probability of combinations (girls/boys) | 1 | |
Python list reverse (recursion) | 1 | |
Sample mean update with new data | 1 | |
Sorting algorithms – bubble/insertion/selection passes | 1 | |
Probability and Statistics Total | 26 | |
Programming, Data Structures and Algorithms | AI – Heuristic admissibility (h1, h2) | 1 |
Array prefix computation (dynamic programming) | 1 | |
Bayesian network joint probability | 1 | |
Binary search comparisons recurrence | 1 | |
Binary search complexity analysis | 1 | |
Binary tree node relationships (height, leaves) | 1 | |
Binary tree properties (height, nodes) | 1 | |
Covariance between random variables | 1 | |
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 recursion | 1 | |
Python recursion – counting tree nodes | 1 | |
Quicksort – swaps count | 1 | |
Relational algebra – SQL tuple verification | 1 | |
Sorting algorithms – bubble/insertion/selection passes | 1 | |
Stack vs queue vs hash table (matching) | 1 | |
Topological sort of DAG | 2 | |
Tree traversal combinations (preorder/inorder/postorder) | 1 | |
Uniform hashing – expected probes | 1 | |
Programming, Data Structures and Algorithms Total | 22 | |
Grand Total | Combination of both 2025 and 2024 GATE question paper | 130 |
Probability and Statistics is the number one chapter with 26 questions; therefore, it is the most important chapter to study. A strategic study plan around the most scoring topics in DA for GATE 2026 can boost your overall rank.
Right behind them are Programming, Data Structures and Algorithms and Aptitude with 22 questions and 20 questions respectively.
In Machine Learning, Linear separability of datasets is mentioned 3 times which is a sign of acute focus towards such a concept.
The topics of visualization and analytical reasoning are centered on such topics as dice folding, cross-sections and geometry, and coloring of graphs (2 appearances each).
The probability subtopics (conditional/joint events, bayes theorem, exponential distribution) are repeated twice, and they are a reminder of common testing on different probability models.
Frequently Asked Questions (FAQs)
More than a few questions are answered with reference to concepts of Linear Algebra, such as eigenvalues, projection matrices, and vector subspaces.
Yes, they come up very commonly, namely when it comes to linear separability, k-means, and decision trees.
Probability and Statistics is given the most weightage as it only contributes to about 20-25% of the paper.
On Question asked by student community
Hello,
The GATE 2026 Agricultural Engineering (AG) paper will have a total of 100 marks. Out of this, 15 marks are for General Aptitude and 85 marks are for core Agricultural Engineering subjects.
The marks are generally distributed among major topics like Engineering Mathematics (12–13 marks), Farm Machinery (10–11), Farm Power (14–15), Soil & Water Conservation (12–13), Irrigation & Drainage (10–12), Agricultural Process Engineering (10–12), and Dairy & Food Engineering (9–10 marks).
The official qualifying marks for GATE 2026 are not yet released. However, based on previous years, the cutoff for the Agricultural Engineering paper is expected to be around 25 marks for General, 22.5 for OBC/EWS, and 16.6 for SC/ST/PwD candidates.
Hope it helps !
Hello,
No,For Mtech you are not eligible to give GATE examination in 2nd year diploma and after completing your diploma degree
Only bachelor's degree holders can give GATE examination
Thank you
Hello,
You can access various GATE Preparation materials from Careers360:
3. GATE Preparation tips for ECE 2026
Hope it helps !
Hello
If you're in 2nd year right now, you can’t register for GATE 2026 just yet.
The exam is only open to students in the 3rd year or beyond in their degree.
It’s mainly because the GATE tests subjects that are usually taught later in your course.
But don’t worry, you are in a great position to start preparing early!
Focus on your basics, practice problem-solving, and build strong concepts now.
By the time you reach 3rd year, you’ll be well ahead of the game.
Hello,
If your GATE 2026 application shows "under scrutiny," continue to monitor your applicant portal and registered email for updates, as this is a routine process where officials verify your details. An "under scrutiny" status does not mean your application is rejected; you may be notified of discrepancies and given a chance to correct them during the application correction window, which opens later. Keep your application details accurate and prepare for the exam while waiting for the correction window to open.
I hope it will clear your query!!
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