GATE DA Exam Analysis 2026: Paper Review And Overall Difficulty Level
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GATE Data Science and Artificial Intelligence 2026 breaks down the paper’s difficulty, question distribution, and major topics immediately after the session ends. Following this analysis lets you assess your performance and adjust your preparation strategy. The GATE DA 2026 on Day 4, Second Shift will take place on February 15, 2:30 pm to 5:30 pm.
GATE Data Science and Artificial Intelligence (DA) Overall Difficulty Level
The overall difficulty level of GATE DA Question Paper 2026 will be determined after reviewing student reactions and exam data. Early feedback suggests a moderate difficulty, with some sections requiring strong analytical and calculation skills. Time management will play a critical role, especially in Numerical Answer Type (NAT) and problem-solving questions.
GATE DA 2026 Section-wise Questions Analysis
A detailed section-wise breakdown of GATE DA 2026 will be shared post-exam, including counts for Multiple-Choice Questions (MCQs), Multiple-Select Questions (MSQs), and Numerical Answer Type (NAT) questions. Expect a total of 65 questions, similar to previous years, with NATs providing a no-negative-marking advantage. Questions will test applied knowledge, statistics, and programming concepts.
| GATE DA 2026 Section-wise Questions Analysis | |
| Types of Questions Asked | Total Number of Questions |
| Multiple-Choice Questions (MCQs) | To Be Released |
| Multiple-Select Questions (MSQs) | To Be Released |
| Numerical Answer Type Questions (NAT) | To Be Released |
Check: GATE DA Answer Key 2026
GATE DA 2026 High-Weightage Topics
Key topics in GATE DA 2026 are likely to follow the standard syllabus, with high-weightage areas including:
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Mathematics & Statistics: Probability, distributions, hypothesis testing, linear algebra, regression, correlation.
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Programming & Algorithms: Python, R, data structures, algorithmic logic, complexity.
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Data Analytics Techniques: Clustering, classification, data preprocessing, and visualization.
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Database Systems: SQL, NoSQL, data querying and manipulation.
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Machine Learning Basics: Supervised/unsupervised learning, evaluation metrics, model selection.
Number of Good Attempts in Data Science and Artificial Intelligence (DA)
Good attempts are estimated after reviewing the question set and candidate input. For GATE DA, aiming for 50–60 accurate attempts across core subjects is considered competitive.
| Number of Good Attempts in Data Science and Artificial Intelligence (DA) | ||
| Paper Name | Paper Code | Number of Good Attempts |
| Data Analytics | DA | To Be Released |
Student Feedback About the GATE DA Paper
Student reactions will be compiled after the exam to provide insights on the overall feel, tough sections, and question patterns. Feedback helps aspirants understand which topics need reinforcement for future attempts.
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