Teacher's Exam Style Analysis

Core Philosophy

"We privilege quality over quantity" — concise, clear, correct.

The teacher tests applied understanding, not memorisation. Every question gives a scenario and asks you to reason about it.

Question Patterns That Repeat Every Exam

Pattern 1: "Evaluate These Suggestions" (EVERY EXAM)

Format: Given model settings + results, evaluate 2-3 suggestions.

How to nail it:

FIRST: diagnose the problem (overfitting or underfitting?)
THEN: for each suggestion, say YES/NO
THEN: explain WHY by connecting to your diagnosis

Scoring: 2 marks each (1 for answer, 1 for reasoning connected to scenario)

Scenario	Diagnosis	What Helps	What Doesn't
Train HIGH, Val LOW	Overfitting	Regularisation, more data, data aug	More epochs, bigger model
Train LOW, Val LOW	Underfitting	Bigger model, more features	Regularisation, dropout

Pattern 2: CNN Dimension Calculation (EVERY EXAM)

Format: Given architecture spec → compute output at each layer → find FC input size.

How to nail it: Write this for EVERY layer:

[Layer Name]
  Input:  [H, W, C]
  Formula: ((H + 2p - f) / s) + 1
  Output: [H', W', C']

Pattern 3: Transformer Two-Part Question (EVERY EXAM)

Format: (a) Explain mechanism X. (b) Why is it useful?

How to nail it: Part (a) = WHAT it does. Part (b) = WHY it matters (concrete benefit).

Pattern 4: Loss Curve / Metric Interpretation (2/3 EXAMS)

Format: Given graph or numbers → diagnose + suggest fix.

Traps the Teacher Sets (And How to Avoid Them)

Trap	What Students Do Wrong	Correct Answer
Underfitting + regularisation	"Use dropout to improve!"	NO — dropout fights overfitting, this is underfitting
Multi-label output activation	"Use softmax"	NO — sigmoid (independent per output)
Zero weight initialisation	"Smaller weights = better"	NO — zero creates symmetry, neurons can't differentiate
More epochs when overfitting	"Train longer to learn more"	NO — worsens overfitting
High accuracy with imbalanced data	"70% accuracy = good model"	Check precision/recall — might just predict majority class
Max vs Avg pooling output size	"Different pooling = different size"	SAME size, only values differ

Sentence Patterns in Questions → What They Want

Question Says	They Actually Want
"Explain if it is likely to improve..."	YES/NO + reasoning linked to the specific scenario
"Describe performance in terms of bias and variance"	Identify overfitting/underfitting from curves
"Briefly justify"	2-3 sentences MAX with the key reason
"Show your calculation steps"	Formula → numbers → result (at each layer)
"What do you think about this model?"	Go BEYOND numbers — what is the model actually doing?
"Explain in your own words"	Show understanding, not textbook recitation

Concepts That Always Appear Together

Bias-Variance ←→ Regularisation ←→ Design Choices
(one question covers all three — master the connections)

CNN Dimensions ←→ Valid/Same Padding ←→ FC Layer Size
(pipeline calculation from start to end)

Transformer ←→ Masked Attention ←→ Positional Encoding ←→ ViT
(mechanism + why it exists)

Loss Curves ←→ Learning Rate ←→ Optimizers
(visual diagnosis skill)

Confusion Matrix ←→ Class Imbalance ←→ Misleading Accuracy
(numbers game — always check precision AND recall)

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