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LLM · Quiz 1 · 26 Oct 2025 (Afternoon)

20 questions · 50 marks · 60 min

Answer key Take as mock

Indian Institute of Technology Madras

BS Degree Programme · Examination & Study Paper

Large Language Models

Quiz 1 · 26 Oct 2025 (Afternoon)

Official Paper

Examination Instructions & Guidelines

Solve on notebook/paper first before checking solutions.
MCQ: Exactly 1 option is correct. Negative marking applies.
MSQ: One or more options correct. Select all to score.
NAT: Numerical response. No negative marking.
Scientific calculator allowed for all calculations.
Official answer key is sealed at the end of this sheet.

Q1MCQ2 Marks·Choose one option
Key ↓
1. ATo increase the numerical precision of the attention scores.
2. BTo reduce the overall computational cost of the attention mechanism.
3. CTo ensure that the sum of the attention scores for each query equals 1.
4. DTo avoid numerical issues and loss of gradients during training.
Q2MCQ2 Marks·Choose one option
Key ↓
Which of the following is true regarding sinusoidal encoding?
1. AThe encoding vector of words present at even positions in the given text sequence uses sine, and the encoding vector of words present at odd positions uses cosine.
2. BThe encoding vector of words present at odd positions in the given text sequence uses sine, and the encoding vector of words present at even positions uses cosine.
3. CThe encoding vector for each word position contains values computed using both sine (for even dimensions) and cosine (for odd dimensions).
Q3MCQ2 Marks·Choose one option
Key ↓
1. A‘content’
2. B‘je’
3. C‘suis’
4. D‘heureux’
Q4MCQ2 Marks·Choose one option
Asked 2 times Key ↓
1. A
2. B
3. C
4. D
5. E
Q5MCQ2 Marks·Choose one option
Key ↓
In the context of language model training, teacher forcing is a technique where:
1. AThe correct target tokens from the training data are used as input for the next time step, regardless of the model’s prediction.
2. BThe model’s predictions are used as the input for the next time step, even if they are incorrect.
3. CThe model is forced to learn without any pre-trained weights.
4. DThe model is forced to correct the input data.
Q6MCQ2 Marks·Choose one option
Key ↓
1. A
2. B
3. C
4. D
Q7MCQ3 Marks·Choose one option
Key ↓
1. A
2. B
3. C
4. D
Q8MCQ3 Marks·Choose one option
Key ↓
1. A
2. B
3. C
4. D
5. E
Q9MCQ3 Marks·Choose one option
Key ↓
For a vocabulary size of 10, how many beams will there be in greedy search, beam search with beam size 3, and exhaustive search at step 1 and step 3, respectively?
1. AStep 1: Greedy: 1, Beam: 1, Exhaustive: 1 Step 3: Greedy: 1, Beam: 1, Exhaustive: 1
2. BStep 1: Greedy: 1, Beam: 3, Exhaustive: 10 Step 3: Greedy: 1, Beam: 3, Exhaustive: 10
3. CStep 1: Greedy: 1, Beam: 3, Exhaustive: 1 Step 3: Greedy: 1, Beam: 3, Exhaustive: 100
4. DStep 1: Greedy: 1, Beam: 3, Exhaustive: 10 Step 3: Greedy: 1, Beam: 3, Exhaustive: 1000
Q10MSQ2 Marks·Choose all correct options
Asked 2 times Key ↓
Which of the following decoding strategies is/are inherently non-deterministic?
1. ABeam
2. BGreedy
3. CNucleus
4. DExhaustive
Q11MSQ2 Marks·Choose all correct options
Key ↓
Which of the following statements about multiple attention heads in a Transformer are true?
1. AThey allow learning diverse local/global relations.
2. BThey reduce memory usage compared to one head.
3. CIncreasing the number of heads while keeping d_(model) fixed, decreases the dimensionality handled by each head.
4. DAll heads necessarily learn completely independent information about the input.
Q12MSQ4 Marks·Choose all correct options
Key ↓
Which of the following statements about the Transformer architecture are true?
1. AMasking (look-ahead mask) is applied in every decoder self-attention layer, not just the first one.
2. BToken embeddings are used in the encoder but not in the decoder.
3. CPositional encoding is required only in the encoder, not in the decoder.
4. DToken embeddings are used in both the encoder and the decoder.
5. EPositional encodings are added to the input embeddings in both the encoder and the decoder.
Q13MSQ4 Marks·Choose all correct options
Key ↓
1. ABeam search with beam size 2 will keep either A or B as per probability whereas Top-2 sampling will keep both A and B.
2. BBeam search with beam size 2 will keep both A and B whereas Top-2 sampling will keep either A or B.
3. CBeam search with beam size 2 will randomly choose any two tokens, whereas Top-2 sampling will always pick the two least probable tokens.
4. DBeam search with beam size 2 will always keep the top-2 most probable tokens, whereas Top-2 sampling will restrict choices to top-2 tokens and then sample one probabilistically.
Q14NAT3 Marks·Write the number
Key ↓
Your answer:
Q15NAT3 Marks·Write the number
Key ↓
Your answer:
Q16NAT2 Marks·Write the number
Key ↓
Your answer:
Read the passage below, then answer Q17–Q20
Q17NAT3 Marks·Write the number
Key ↓
How many next-token prediction targets are generated during training a full batch?
Your answer:
Q18NAT2 Marks·Write the number
Key ↓
For one sequence, how many non-zero attention scores remain in the masked attention score matrix (per head)?
Your answer:
Q19NAT2 Marks·Write the number
Key ↓
Your answer:
Q20MSQ2 Marks·Choose all correct options
Key ↓
During autoregressive inference with GPT:
1. AThe model can generate all tokens in parallel.
2. BEach new token is generated conditioned on previously generated tokens.
3. CTeacher forcing is used to stabilize outputs.
4. DBeam search or other decoding strategies are commonly used to decide the next token.

Official Key

Master Answer Key & Explanations

Official answer keys from IIT Madras BS past papers. Click any question to review.

Reveal Key

Q1D

Q2C

Q3C

Q4C

Q5A

Q6C

Q7A

Q8C

Q9D

Q10C

Q11A, C

Q12A, D, E

Q13B, D

Q141152

Q1599 to 101

Q1632768

Q1732736

Q18524800

Q19589824

Q20B, D

Questions and answer keys are reproduced from the official IIT Madras BS papers. BTechi is not affiliated with IIT Madras.