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Explain Transformers and deploy an LLM safely

MicrosoftMachine Learning Engineer · Technical Screen
EasyMachine Learning EngineerTechnical ScreenML System Design

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This question evaluates understanding of Transformer architectures and practical LLM deployment competencies, covering attention mechanisms, token and positional representations, computational complexity, and production concerns like latency, cost, quality, safety, and privacy.

Answer the following LLM-focused questions.

1) Transformer basics

  • What problem does the Transformer architecture solve compared with RNNs?
  • Explain the main components:
  • token embeddings and positional information
  • self-attention (including what "Q/K/V" are)
  • multi-head attention
  • feed-forward network, residual connections, layer norm
  • What is the computational complexity of full self-attention with respect to sequence length (L)?

2) Real-world LLM deployment

You are asked to deploy an LLM-powered feature (e.g., internal assistant or customer support bot).

  • List the main real-world challenges (latency, cost, quality, safety, privacy, etc.).
  • Propose a deployment architecture and concrete mitigations for those challenges.
  • Describe how you would evaluate the system offline and monitor it online after launch.