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 is updated here. The GATE Data Science & Artificial Intelligence (DA) 2026 paper was reported to be mathematics-focused and ranged from moderate to slightly tough according to student feedback. The MSQ questions were moderate in difficulty, while analytical and problem-solving questions required careful calculation and time management. Overall, the paper tested conceptual understanding along with practical data science applications.
GATE DA 2026 Section-wise Questions Analysis
The section-wise distribution of GATE DA 2026 is now available. The paper followed the standard pattern of 65 questions including Multiple-Choice Questions (MCQs), Multiple-Select Questions (MSQs), and Numerical Answer Type (NAT) questions. A notable share of NAT questions offered a no-negative-marking advantage. Overall, the questions focused on applied knowledge, statistics, mathematics, and programming concepts.
| GATE DA 2026 Section-wise Questions Analysis | |
| Types of Questions Asked | Total Number of Questions |
| Multiple-Choice Questions (MCQs) | 33 |
| Multiple-Select Questions (MSQs) | 14 |
| Numerical Answer Type Questions (NAT) | 18 |
Check: GATE DA Answer Key 2026
GATE DA 2026 High-Weightage Topics
Key topics in GATE DA 2026 followed 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.
Check: GATE 2026 DA Expected Cut Off
Number of Good Attempts in Data Science and Artificial Intelligence (DA)
Good attempts are estimated after reviewing the question set and the candidate's 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 indicate that the paper was largely mathematics-focused. A few questions from Artificial Intelligence were relatively easy to manage, and DBMS questions were considered straightforward; some were tricky. However, several mathematics problems were tricky and time-consuming, making accuracy and careful calculation important for good attempts.
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