Explain Transformers and deploy an LLM safely

Q: Explain Transformers and deploy an LLM safely

This is a ML System Design interview question from Microsoft for Machine Learning Engineer roles. View the full question and solution on PracHub.

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Question

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.

Explain Transformers and deploy an LLM safely

1) Transformer basics

2) Real-world LLM deployment

Solution

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