GATE 2026: DS & AI Preparation Guide

If you're preparing for the GATE 2026 Data Science and Artificial Intelligence (DA) paper, this comprehensive guide will help you navigate the syllabus, understand the exam pattern, and get access to focused MCQs and preparation resources. The DA paper is a relatively new addition to GATE, and this page is designed to provide a structured roadmap for your preparation.

General Aptitude for GATE 2026 – Common for All Papers

The General Aptitude (GA) section is a crucial part of the GATE DA paper, contributing 15% of the total marks. This section is designed to test your command over basic language skills, mathematical reasoning, and analytical thinking. Here's what you need to focus on:

📘 Verbal Aptitude

• Basic English grammar (tenses, articles, conjunctions, prepositions)

• Vocabulary in context, idioms, and phrases

• Reading comprehension and sentence sequencing
  
  

📐 Quantitative Aptitude

• Data interpretation using graphs, tables, and charts

• Numerical computation: ratios, percentages, exponents, logarithms

• Permutations, combinations, elementary statistics, and geometry
  
  

🧠 Analytical & Spatial Aptitude

• Logical deduction, induction, analogies, numerical reasoning

• Shape transformations, paper folding, mirroring, 2D and 3D visualization
  
  

Core Subject Syllabus for GATE 2026 Data Science and AI

The core part of the DA paper is divided into seven key subject areas. Below is a breakdown of each domain along with major topics covered:

🎲 Probability and Statistics

• Probability rules, Bayes’ theorem, expectation, variance

• Discrete & continuous distributions: Bernoulli, Binomial, Normal, Poisson

• Confidence intervals, hypothesis testing (z-test, t-test, Chi-square)

• Correlation, covariance, random variables, Central Limit Theorem
  
  

➕ Linear Algebra

• Vector spaces, subspaces, matrices (orthogonal, projection, idempotent)

• Linear dependence, eigenvalues & eigenvectors

• LU decomposition, SVD, determinants, Gaussian elimination
  
  

📈 Calculus and Optimization

• Functions, limits, continuity, differentiability

• Taylor series, maxima and minima

• Optimization with single-variable functions
  
  

💻 Programming, Data Structures & Algorithms

• Python programming basics

• Data structures: stacks, queues, trees, hash tables

• Sorting algorithms (bubble, insertion, quicksort)

• Graph algorithms and traversals
  
  

🗃️ Database Management & Warehousing

• ER model, relational algebra, SQL queries

• Indexing, normalization, file organization

• Data transformation techniques and warehouse schema modeling
  
  

🤖 Machine Learning

• Supervised learning: regression, classification, decision trees, SVM

• Neural networks: perceptrons, MLPs, feedforward

• Unsupervised learning: k-means, PCA, clustering algorithms

• Model evaluation: bias-variance, cross-validation techniques
  
  

🧠 Artificial Intelligence

• Search strategies: BFS, DFS, A*, adversarial search

• Logic: propositional and predicate

• Reasoning under uncertainty, inference techniques, independence modeling
  
  

GATE 2026 DA Exam Pattern – What to Expect

GATE 2026 for DA will be a computer-based test held for a total of 100 marks, and the duration of the paper is 3 hours. The paper consists of 65 questions:

• General Aptitude: 15 marks (10 questions)

• Core Subjects: 85 marks (55 questions)

• Total: 100 marks
  
  

✍️ Marking Scheme

• 1-mark and 2-mark questions

• Negative marking applies only to MCQs:

  • For 1-mark MCQs: ⅓ mark deduction

  • For 2-mark MCQs: ⅔ mark deduction

• NAT (Numerical Answer Type) questions have no negative marking
  
  

📄 Download GATE 2026 DA Syllabus PDF

You can download the official GATE Data Science and AI syllabus 2026 PDF below for offline reference and preparation planning.

👉 Download Syllabus PDF

📚 Practice MCQs by Topic – GATE 2026 DS & AI

Prepare smarter with these topic-specific multiple choice questions based on the official GATE 2026 Data Science and Artificial Intelligence syllabus:

🔹 Machine Learning

• Supervised Learning MCQs

• Unsupervised Learning MCQs

• Regression Analysis MCQs

• Clustering MCQs

• Decision Tree MCQs

• Ensemble Learning MCQs
  
  

🔹 Artificial Intelligence

• Search Algorithms MCQs

• Bayesian Reasoning MCQs
  
  

🔹 Neural Networks & Deep Learning

• Artificial Neural Networks MCQs

• Neural Network MCQs

• Recurrent Neural Networks (RNN) MCQs

• Convolutional Neural Networks (CNN) MCQs

• Generative Adversarial Networks (GAN) MCQs

• Deep Learning MCQs
  
  

🔹 Dimensionality Reduction

• Principal Component Analysis & Dimensionality Reduction MCQs
  
  

🔹 Programming & Data Structures

• Python Programming MCQs

• Data Structures MCQs
  
  

Why This Page Matters – Your Ultimate GATE 2026 DA Resource Hub

Since the DA paper is relatively new, aspirants often struggle to find reliable study material. Our team at aimcqs.com is committed to:

• Creating topic-wise MCQ practice sets

• Sharing study notes and summaries for each subject

• Providing mock tests and detailed answer explanations

• Regularly updating resources based on syllabus revisions
  

Start your preparation with confidence and strategy. Bookmark this page to stay updated as we continuously expand each subject section.