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Deep Learning Practice Quiz 1: 15 March 2026 (January 2026 term)

Question 1

+2 marksOne correct option

Consider subword tokenization methods such as Byte Pair Encoding (BPE) and WordPiece as employed in transformer-based language modeling pipelines. Which of the following statements correctly describes their fundamental properties?

  1. A

    They always split words at true morpheme boundaries (the smallest units of meaning, e.g., “un-” + “break” + “able”).

  2. B

    They learn merge rules from corpus statistics (frequency/likelihood), not token semantics

  3. C

    They eliminate (Out Of Vocabulary) OOV by ensuring every word is a single token

  4. D

    They generalize perfectly to out-of-domain text without increasing sequence length.

Question 2

+2 marksOne correct option

Consider two subword tokenizers trained on the same text corpus. Tokenizer A uses a vocabulary of 8,000 tokens, whereas Tokenizer B uses a vocabulary of 64,000 tokens. Which of the following statements most accurately characterizes the implications of these vocabulary sizes for training efficiency in transformer-based language models?

  1. A

    Larger vocabulary always reduces sequence length and total compute.

  2. B

    Smaller vocabulary always improves semantic alignment.

  3. C

    Larger vocabulary reduces sequence length but increases embedding parameters.

  4. D

    Vocabulary size has no effect once model is pretrained.

Question 3

+2 marksOne correct option

A subword tokenizer decomposes rare chemical entity names into a large number of fragments. During downstream fine-tuning, the model exhibits degraded performance on a chemical named entity recognition (NER) task. Which of the following represents the most principled corrective action?

  1. A

    Increase the dropout rate during fine-tuning.

  2. B

    Retrain the tokenizer using an in-domain (chemical) corpus.

  3. C

    Reduce the batch size during training.

  4. D

    Apply label smoothing to the loss function.

16 more questions in this paper

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More on the Deep Learning Practice Quiz 1 15 Mar 2026 paper

The IIT Madras BS Deep Learning Practice (Deep Learning Practice) Quiz 1 paper sat on 15 Mar 2026, in the January 2026 term: 19 questions for 50 marks in 120 minutes. The first 3 questions are below. Sign in with Google — it is free — to see the whole paper with its answers and explanations, in learning mode or as a timed mock test.

FeatureDeep Learning Practice Quiz 1 15 Mar 2026 at a glance
TermJanuary 2026 term
SubjectDeep Learning Practice
Course codeBSDA5013
Questions19
Marks50
Duration120 min
MCQ10
MSQ6
Written3
Official paperDeep Learning Practice 15 Mar 26
Negative markingNo negative marking.
Updated

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