Explain tokenization and Transformer variants

Q: Explain tokenization and Transformer variants

This question evaluates understanding of tokenization techniques and Transformer architecture, covering competencies in subword and SentencePiece-style tokenizers, Transformer block internals, and comparisons of modern architectural variants and trade-offs.

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Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

Q: What difficulty level is this interview question?

This is a medium difficulty Machine Learning question, commonly asked during Technical Screen rounds at Netflix.

Q: What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Netflix during technical interviews.

Question

Tokenization and Transformer Architecture Deep Dive

You are asked to explain common tokenization approaches and modern Transformer design choices used in large language models.

Answer the following:

SentencePiece

What is SentencePiece, and how does it work at a high level?

Tokenizers used in BERT and typical Transformer-based LMs

Which tokenizers do BERT and common decoder-only LMs (e.g., GPT-style, LLaMA, Qwen) typically use, and why?

Transformer block internals

Enumerate the core components inside a Transformer block and briefly describe the role of each.

Architectural comparisons and design trade-offs

Compare a vanilla Transformer (Vaswani et al., 2017) to modern LLaMA and Qwen architectures.
Discuss the benefits and trade-offs of choices such as Mixture-of-Experts (MoE), RMSNorm, and rotary positional embeddings (RoPE).

Explain tokenization and Transformer variants

Quick Overview

Explain tokenization and Transformer variants

Tokenization and Transformer Architecture Deep Dive

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