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Transformers in NLP MCQ
10 Conceptual Questions with In-Depth Technical Explanations
Deep questions on encoder-decoder structures, self-attention, positional encodings, feedforward sublayers, and layer norms.
Transformers in NLP Questions
Select an answer to reveal detailed technical explanation and formulas.
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What are the core components of a standard Transformer block?
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Why do Transformers require Positional Encodings added to input token embeddings?
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What is the difference between BERT and GPT in terms of Transformer architecture?
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What is the purpose of the Causal (Look-Ahead) Attention Mask in Transformer Decoders (like GPT)?
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What is Rotary Position Embedding (RoPE, Su et al., 2021) used in modern LLMs (LLaMA, Mistral)?
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What is the computational complexity of standard Full Multi-Head Self-Attention with respect to sequence length N?
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What is FlashAttention (Dao et al., 2022)?
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What is the difference between Pre-LN (Pre-LayerNorm) and Post-LN (Post-LayerNorm) Transformer architectures?
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What is the SwiGLU feed-forward network layer used in LLaMA and PaLM?
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About Transformers in NLP Interview Questions & Quiz
These Transformers in NLP multiple choice questions are curated to prepare engineers, researchers, and data science students for top technical interviews, machine learning certifications, and university exams.
Every MCQ incorporates core mathematical formulas, architectural tradeoffs, and practical algorithmic considerations. Practicing these questions guarantees a solid foundational and conceptual mastery of Transformers in NLP.