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Explain tokenization and Transformer variants

NetflixMachine Learning Engineer · Technical Screen
MediumMachine Learning EngineerTechnical ScreenMachine Learning

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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.

Explain what SentencePiece is and how it works. State which tokenizers BERT and typical Transformer-based LMs commonly use and why. Enumerate the core components within a Transformer block and describe their roles. Compare a vanilla Transformer to LLaMA and Qwen architectures, and discuss the benefits and trade-offs of choices such as Mixture-of-Experts (MoE), RMSNorm, and rotary positional embeddings (RoPE).