Free GATE DA mock

One GATE DA paper in the real exam format: 65 questions across one timed section, 3 hr on a server-controlled clock.

About the real exam India's fastest-growing GATE paper — your gateway to AI/ML research at IISc, the IITs, and the IIITs.

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GATE DA — Full Mock
65 questions · 3 hr
Opens your Free Mock selection · you confirm before your one attempt is used

Free, reversible until you begin · one attempt per account · no retake, no card.

About the exam

The GATE DA in four panels

The exam

GATE DA (Data Science and Artificial Intelligence) is the newest paper in the Graduate Aptitude Test in Engineering — introduced in 2024 by IISc Bangalore, then IIT Roorkee (2025), IIT Guwahati (2026), and IIT Madras (2027). It was created to fill a real gap: India's premier M.Tech programmes in AI, machine learning, and data science had no dedicated GATE paper, forcing aspirants to enter via CS, EE, or Mathematics. The single 3-hour CBT covers 65 questions across 7 core sections plus General Aptitude, totalling 100 marks. Scores stay valid for three years and are accepted at IISc DSAI, IIT Madras' Robert Bosch Centre for Data Science and AI, IIT Bombay's C-MInDS, IIT Hyderabad's AI department, IIT Delhi's Yardi School of AI, and all IIT M.Tech AI/DS programmes — plus MS and PhD admissions at IIIT Hyderabad, IIIT Delhi, and IIIT Bangalore. It is also an emerging qualifying gate for AI Researcher and Scientist-grade positions at BARC, ISRO, DRDO, and the National Quantum Mission labs.

The candidates

  • Final-year B.Tech / B.E. / MCA students from CS, IT, Mathematics, Statistics, EE, or allied branches targeting an M.Tech in AI/DS at IISc or an IIT.
  • B.Sc. / M.Sc. graduates in Mathematics, Statistics, or Computer Science wanting to pivot into AI/ML research via a CFTI M.Tech.
  • Working professionals (1–5 years in software, data engineering, or analytics) preparing to switch into applied ML or AI research.
  • Job-seekers targeting AI Researcher / Data Scientist roles at BARC, ISRO, DRDO, NTRO, and the emerging AI-focused central labs that now use GATE DA as their first-round filter.
  • GATE CS aspirants picking up DA as a paired second paper — Programming + Math + Algorithms overlap is high, competition is roughly half of CS.

How people prepare for it

  • Probability and Statistics is the single biggest scoring section (~17 marks) AND the most tractable — start here. Solve at least 50 problems each on Bayes theorem, expectation/variance, and the named distributions (binomial, Poisson, normal) before moving on.
  • Machine Learning (~16 marks) rewards conceptual depth + computation practice. Bishop's PRML and Hastie/Tibshirani/Friedman's ESL are the canonical references. Don't skip the math behind ridge regression, SVM dual formulation, and PCA — GATE consistently tests derivations, not just terminology.
  • Programming (~17 marks) is Python-only. Practise 30+ problems each on basic DS (stacks, queues, trees, hash tables), and at least 15 each on the two divide-and-conquer sorts (mergesort, quicksort) including correctness proofs and recurrence solving.
  • Linear Algebra and Calculus/Optimization together are ~20 marks and overlap heavily with ML — every gradient-descent question, every eigendecomposition, every Lagrangian touches both. Treat them as one combined effort.
  • Practise mixed +1/+2 marking with proportional negatives on weekly full-length mocks. Strategy under negative marking is its own skill, separate from subject mastery — most GATE DA candidates lose 8–12 marks per paper to bad guessing alone.
  • Solve all GATE DA 2024 and 2025 PYQs end-to-end — these are the only two papers in existence at the start of GATE 2027 prep. Each section yields 8–15 PYQ items; exhaust them, then move to GATE CS Math sections + GATE Mathematics paper for additional rigor on shared topics.
~57,000
Appeared (2025)
Third-highest GATE paper despite only its second year — demand growing ~20% YoY
26.2 / 100
Qualifying cut-off
General category, 2025 (OBC: 23.5, SC/ST: 17.4)
3 years
Score validity
GATE 2027 scores valid for admissions / hiring through 2030
~870 / 1000
IISc DSAI cut-off
Indicative M.Tech admission cut-off, 2025 cycle
17 marks
Top section weight
Probability and Statistics — also the most tractable section to drill
1
Languages
Python only — no C/C++/Java in the syllabus

Figures about the real exam, as recorded in the Myndaq course. They describe the real sitting, not this free mock paper - check current details with the exam body before you book.

What it opens up

  • Master Probability, Linear Algebra, and Calculus to the depth modern ML demands — the same mathematical foundation top AI labs assume.
  • Build first-principles understanding of regression, classification, clustering, and dimensionality reduction — not just sklearn API knowledge.
  • Develop fluent Python data-manipulation and algorithm-implementation skills (basic DS, search, sort, graph traversals).
  • Practise relational thinking with SQL, ER modelling, normalization, and the warehousing concepts that underlie production data systems.
  • Reason about AI search, logic, and Bayesian inference — the classical AI foundation modern systems still build on.
  • Strategise under negative marking — pick attempt sets, manage time across 65 mixed-difficulty items, and avoid the 10-mark guess-tax that sinks most DA candidates.

Course context for the real GATE DA.

Marks and results

What you get back

What this free mock returns

You get your raw marks and per-section scores. No scaled score, band, rank or percentile is estimated for this paper.

  • Scored out of 100 raw marks, with per-section totals.
  • Wrong answers can cost marks where the paper defines it - the per-section marks in the paper map are the marks the scorer applies.

How the real GATE DA is scored

GATE scores are reported as both raw marks (out of 100) and a normalised GATE score (out of 1000) — IISc / IIT cutoffs are quoted in the 1000-scale, PSU cutoffs in the 100-scale. The General-category qualifying mark for DA in 2025 was 26.2 / 100, dropping to 23.5 for OBC-NCL / EWS and 17.4 for SC / ST / PwD. The qualifying mark is only the floor; competitive M.Tech in AI/DS programmes and PSU AI Researcher roles require considerably higher.

01850+ / 1000 (≈ 65+ / 100)Realistic shot at IISc CSA / DSAI M.Tech, IIT Madras RBCDSAI, IIT Bombay C-MInDS, IIT Delhi Yardi School of AI. Top-tier PSU AI Researcher interviews (BARC, ISRO).
02750 – 850 / 1000Strong M.Tech AI/DS seats at IIT-Roorkee, IIT-Guwahati, IIT-Hyderabad, IIT-BHU, NIT Trichy AI track. Direct AI Engineer hiring at DRDO and NTRO.
03650 – 750 / 1000M.Tech AI/DS at lower-tier IITs (Patna, Bhilai, Jodhpur), top NITs (Surathkal, Warangal, Calicut), and most IIITs. Useful for industry data scientist roles.
04Qualifying (26.2+ / 100, General)Clears the floor for M.Tech application eligibility at most CFTIs and several PSU written rounds.

Context only - this free mock returns: You get your raw marks and per-section scores. No scaled score, band, rank or percentile is estimated for this paper.

The published paper

The paper, start to finish

GATE DA — Full Mock65 · 3 hr · Scored out of 100 marks; wrong answers can cost marks
Calculator

One cell per question, grouped by section.

Navigation: move freely between questions within a section.

Be clear about this

What differs or is limited

  • This free mock follows the same paper shape, timing, marking scheme and navigation as our paid GATE DA 2027 mock series. It is scored automatically. It does not include tutor explanations, analysis dashboards or practice content.
  • Includes the virtual calculator and free navigation, as in the real exam.
Fidelity

How this mirrors the real format

  • The paper structure is the real format: a single timed section, 65 questions in total.
  • Timing is server-controlled for the full 3 hr -- closing the tab does not pause the clock, exactly like our paid mock series.
  • Marking follows the published paper: the per-section marks shown in the paper map are the marks the scorer applies.
  • Navigation is free within a section: you can review and change answers before you submit.
  • An on-screen calculator is provided in the sections where the exam allows one.

"Real format" means the paper shape, timing, marking and navigation above - not the official exam software, its visual identity, or any affiliation with the exam body.

Before you begin

Device and setup checklist

  • A stable internet connection for the full duration.
  • An on-screen calculator is provided where the exam allows one.
  • A laptop or desktop is recommended for the closest exam experience.
One attempt

One attempt, explicitly

Adding this mock to your account is free and reversible - you can change the exam any time before you begin. Only the confirmed Begin action uses your one attempt. No retake, no reset, no card.

Questions

Frequently asked

How many attempts do I get?
One. Every account gets exactly one free mock, on one exam. You can change the chosen exam freely until you begin the paper; after that the attempt is permanently used and there is no retake.
What does the result include?
You get your raw marks and per-section scores. No scaled score, band, rank or percentile is estimated for this paper.
Is the timing real?
Yes. The paper runs 3 hr on a server-controlled clock, section by section. Closing the tab does not pause it.
Do I need to pay or add a card?
No. The free mock needs only a Myndaq account. The paid course unlocks explanations, practice content and the full mock series.

About the real GATE DA

How is GATE DA scored, given the mixed 1-mark / 2-mark questions?
65 questions total. The 10 GA questions split as 5×1-mark + 5×2-mark (15 marks). The 55 subject questions split as 25×1-mark + 30×2-mark (85 marks). MCQs carry negative marking: −1/3 for a wrong 1-mark answer, −2/3 for a wrong 2-mark answer. MSQs and NATs have NO negative marking, so attempt them freely. The raw / 100 score is then normalised by the organising IIT to a / 1000 GATE Score that IITs and PSUs use for ranking.
Should I pick GATE DA over GATE CS if my goal is AI/ML?
If your goal is specifically an M.Tech in AI, DS, or ML at IISc or an IIT — yes, DA is now the cleaner route. IISc DSAI, IIT Madras RBCDSAI, IIT Bombay C-MInDS, and IIT Delhi Yardi School of AI all accept DA scores. Competition is ~one-third of GATE CS (57K vs 171K). If you want optionality across systems / CS roles + AI roles + PSU hiring, GATE CS is still broader. Many candidates write both — the syllabus overlap is high enough that the marginal prep cost is moderate.
How much Python do I really need? I'm a C/C++ background.
Enough to read code traces fluently — lists, tuples, dicts, sets, comprehensions, slicing, function arguments, and the core idioms (enumerate, zip, sorted, map/filter). You don't need to be production-fluent. GATE DA questions on Python are conceptual, not API-deep. Two weeks of focused practice with a book like Lutz's Learning Python is enough for a strong-CS background to handle every Python question on the paper.
What's the difference between MCQ, MSQ, and NAT, and how should I attempt them?
MCQ = single correct option (4 choices) — has negative marking. MSQ = Multiple Select Question, one or more correct (4 choices) — NO negative marking, but you must mark ALL correct options to get the marks (no partial credit). NAT = Numerical Answer Type — type a number, NO negative marking. Strategy: attempt every MSQ and every NAT (free shots). For MCQs, only attempt if you can confidently eliminate at least one option.
Is the syllabus stable? Should I worry about GATE 2027 changing things?
Very stable. The GATE DA syllabus has not changed since introduction in 2024 — IISc → IIT Roorkee → IIT Guwahati all used identical sections. IIT Madras is unlikely to revise for 2027 since the paper is still establishing itself. Plan against the GATE 2026 (IIT Guwahati) syllabus and you'll be safe.
Can I crack GATE DA without a CS background, coming from a Math or Statistics degree?
Yes — and arguably you start ahead. Probability/Statistics + Linear Algebra + Calculus together are ~37 marks (over a third of the paper) and align perfectly with a Math/Stats B.Sc./M.Sc. You'll need to invest in Python + DS (~17 marks) and Database (~6 marks), but the math foundation lets you crack ML and AI sections much faster than a CS student would crack the math.
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