How do I approach Machine Learning interview questions?

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

What difficulty level is this interview question?

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

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Amazon during technical interviews.

Explain attention and Transformers | Amazon Interview Question

Quick Overview

This question evaluates understanding of scaled dot-product self-attention, Transformer encoder–decoder architecture (including positional encodings, residuals and normalization), distinctions between BERT and GPT pretraining/usage, and competencies in attention math, masking, and time/memory complexity and transfer-learning/inference trade-offs.

Scaled Dot-Product Self-Attention, Transformer Architecture, and BERT vs GPT

You are interviewing for a software engineer role focused on machine learning. Explain the core math and design choices behind Transformers and how they translate to practical trade-offs in transfer learning and inference.

1) Scaled Dot-Product Self-Attention

Derive and define the following:

Queries (Q), Keys (K), Values (V) and how they are computed from inputs
The scaling factor and why it is needed
Masking (padding and causal)
Softmax over attention logits
Time and memory complexity (including multi-head and autoregressive decoding)

2) Transformer Architecture

Explain the encoder–decoder Transformer architecture, including:

Encoder vs decoder stacks and their sublayers
Residual connections and the normalization strategy (pre-norm vs post-norm)
Positional encoding (sinusoidal and alternatives)

3) BERT vs GPT

Compare BERT and GPT in terms of:

Pretraining objectives
Architectural differences
Typical downstream usage
How these choices affect transfer learning and inference behavior/performance

Quick Overview

Scaled Dot-Product Self-Attention, Transformer Architecture, and BERT vs GPT

1) Scaled Dot-Product Self-Attention

Derive and define the following:

Queries (Q), Keys (K), Values (V) and how they are computed from inputs
The scaling factor and why it is needed
Masking (padding and causal)
Softmax over attention logits
Time and memory complexity (including multi-head and autoregressive decoding)

2) Transformer Architecture

Explain the encoder–decoder Transformer architecture, including:

Encoder vs decoder stacks and their sublayers
Residual connections and the normalization strategy (pre-norm vs post-norm)
Positional encoding (sinusoidal and alternatives)

3) BERT vs GPT

Compare BERT and GPT in terms of:

Pretraining objectives
Architectural differences
Typical downstream usage
How these choices affect transfer learning and inference behavior/performance

Explain attention and Transformers

Quick Overview

Scaled Dot-Product Self-Attention, Transformer Architecture, and BERT vs GPT

1) Scaled Dot-Product Self-Attention

2) Transformer Architecture

3) BERT vs GPT

Solution

Submit Your Answer

Explain attention and Transformers

Quick Overview

Scaled Dot-Product Self-Attention, Transformer Architecture, and BERT vs GPT

1) Scaled Dot-Product Self-Attention

2) Transformer Architecture

3) BERT vs GPT

Solution

Submit Your Answer