100+ Free GATE DA Practice Questions

General Aptitude (GA)

Verbal aptitude (grammar, vocabulary, comprehension, analogy) and quantitative aptitude (data interpretation, reasoning, basic numeric problem-solving). Compulsory across all GATE papers — 15 marks of the 100 total.

Probability and Statistics

Counting and combinatorics, probability axioms, sample space and events, conditional probability and Bayes' theorem, discrete and continuous random variables, expectation and variance, key distributions (uniform, exponential, Poisson, binomial, normal), Central Limit Theorem, sampling distributions, confidence intervals, hypothesis testing (Z, t, chi-square), correlation, and linear regression.

Linear Algebra

Vector spaces, subspaces, linear independence and span, matrices and matrix operations, eigenvalues and eigenvectors, rank, nullity, Rank-Nullity theorem, LU and QR decompositions, and Singular Value Decomposition (SVD).

Calculus and Optimization

Functions of one and several variables, limits, continuity, differentiability, partial derivatives, gradient and chain rule, maxima and minima of single- and multi-variable functions, constrained optimization with Lagrange multipliers, and gradient descent for unconstrained optimization.

Programming, Data Structures and Algorithms

Python programming. Linear data structures (arrays, stacks, queues, linked lists), trees (binary, BST, balanced), hash tables. Sorting and searching algorithms, complexity analysis. Graph algorithms — BFS, DFS, shortest path (Dijkstra), Minimum Spanning Tree (Kruskal, Prim).

Database Management and Warehousing

ER models and ER-to-relational mapping, relational algebra, SQL (DDL, DML, joins, aggregates, subqueries), integrity constraints, functional dependencies, normalization (1NF, 2NF, 3NF, BCNF). Data warehouse concepts — star/snowflake schema, ETL, OLAP operations.

Machine Learning

Supervised learning — linear and logistic regression, k-NN, decision trees, naive Bayes, SVM, neural networks and backpropagation, ensemble methods (bagging, random forest, gradient boosting), evaluation metrics (accuracy, precision, recall, F1, ROC AUC), bias-variance tradeoff, regularization (L1, L2). Unsupervised — k-means and hierarchical clustering, PCA and dimensionality reduction. Reinforcement learning basics — MDPs and Q-learning.

Artificial Intelligence

Search techniques — uninformed (BFS, DFS, uniform-cost), informed (best-first, A* with admissibility/consistency), local search. Knowledge representation and reasoning — propositional and first-order logic. Reasoning under uncertainty — probability, Bayesian networks, conditional independence. Basics of natural language processing (tokenization, BoW, TF-IDF).