Exam Intelligence

What the last two papers actually asked — counted, not guessed. Historical trends only: a topic appearing twice does not guarantee it appears a third time.

Read this honestly: these are frequencies from Dec 2025 and May 2026 only. Two papers is a small sample. Use it to prioritise revision — never as a promise about the next paper.

Mathematics for Computer Engineering (N2113111)

Dec 2025: 93 marks of questions analysed · May 2026: 93 marks · 19 of 19 topics appeared in both papers.

TopicDec 2025 QsMay 2026 QsTotalMarks
Eigenvalues & eigenvectors22418
Full Fourier expansion22421
Diagonalisation11211
Cayley–Hamilton theorem1128
Lagrange multipliers (NLPP)11212
Solving an LPP (Simplex method)11211
LPP standard form & dual1126
Chinese Remainder Theorem (CRT)11210
RSA algorithm11212
Remainder by modular arithmetic1126
Finding an in a Fourier expansion1126
Spearman rank correlation1126
Karl Pearson correlation coefficient11210
Regression lines1129
Fitting a straight line y = a + bx1128
Moments (raw moments, moments about origin / mean)1127
Moment generating function (MGF)1127
Poisson distribution1128
Normal distribution11210

No question appeared word-for-word in both papers. Topics repeat; exact wordings change.

Discrete Structures & Graph Theory (N2113112)

Dec 2025: 100 marks of questions analysed · May 2026: 90 marks · 13 of 15 topics appeared in both papers.

TopicDec 2025 QsMay 2026 QsTotalMarks
Mathematical induction11214
Logical equivalence11210
Transitive closure / Warshall11216
Hasse diagram / lattice11214
Equivalence relation11210
Function composition11214
Inclusion–exclusion11216
Recurrence relation11214
Pigeonhole principle11210
Group under × mod 711216
Ring11216
Graph isomorphism11216
Euler / Hamiltonian paths & circuits11214
Truth table / access expression1015
Mapping: function / one-one / onto / many-one1015

No question appeared word-for-word in both papers. Topics repeat; exact wordings change.

Analysis of Algorithms (N2113113)

Dec 2025: 93 marks of questions analysed · May 2026: 93 marks · 6 of 26 topics appeared in both papers.

TopicDec 2025 QsMay 2026 QsTotalMarks
Backtracking family12311
Recurrence solving1126
Merge Sort (FIXED 10 MARKS)11220
Fractional Knapsack1128
Multistage Graph11215
LCS — Longest Common Subsequence11213
KMP String Matching0228
Selection Sort1015
Insertion Sort0115
Asymptotic notations0115
Big Theta (Θ)1015
Binary Search1013
Divide and Conquer0113
Kruskal's MCST10110
Dijkstra's shortest path01110
Prim's MST0115
Greedy general method1013
0/1 Knapsack by DP01110
Floyd-Warshall1015
Principle of Optimality1013
Graph Coloring1015
N-Queen Problem10110
15-puzzle0115
Sum of Subset0115
Rabin-Karp1015
Naïve String Matching1013

No question appeared word-for-word in both papers. Topics repeat; exact wordings change.

Computer Organization & Architecture (N2113114)

Dec 2025: 95 marks of questions analysed · May 2026: 95 marks · 10 of 19 topics appeared in both papers.

TopicDec 2025 QsMay 2026 QsTotalMarks
Number conversion11210
Non-restoring division11216
Booth's algorithm11214
8086 architecture11216
Cache mapping11216
Memory hierarchy / characteristics11212
Associative memory11210
I/O methods11214
Pipeline hazards11214
Flynn's classification11213
IEEE 754 floating point0118
Microprogrammed control unit1018
Hardwired vs Softwired CU0117
Horizontal vs Vertical microinstruction1015
Addressing modes of 80861015
ISA / PCI / USB buses1015
Bus arbitration1015
80386DX operation modes0117
Instruction pipeline0115

1 question(s) appeared word-for-word in both papers:

  • explain flynn's classification.

Generative AI (Open elective)

Dec 2025: 100 marks of questions analysed · May 2026: 52 marks · 9 of 20 topics appeared in both papers.

TopicDec 2025 QsMay 2026 QsTotalMarks
GPT architecture & working12317
RNN — architecture & working11213
LLM — Large Language Models11210
Generative AI — definition & scope1127
GPT variants11210
Ethics in Generative AI11210
Generative AI tools / applications11210
Pre-training1129
CNN — architecture & working11213
Challenges in Generative AI1018
Importance in five real-world domains1015
Traditional vs modern language models1015
Transformer architecture0115
LSTM0112
Tokenization in transformers0112
Chat-GPT1017
Multi-head mechanism in GPT1017
Self-attention in GPT1015
GPT and Chat-GPT — definition0112
Importance of data in training1015

No question appeared word-for-word in both papers. Topics repeat; exact wordings change.

Practice sets — questions only

Every question from both papers, grouped by topic. Work them, then check yourself against your own notes — answers are not published here.