Transformer Architecture and Attention Questions

Comprehensive understanding of Transformer architecture and attention mechanisms including the principles of self attention where queries keys and values are used to compute attention weights with appropriate scaling. Understand scaled dot product attention and multi head attention and why parallel attention heads improve representational capacity. Know positional encoding schemes including absolute positional encodings relative positional encodings rotary position encodings and alternative methods for injecting order information. Be able to explain encoder and decoder components feed forward networks residual connections and layer normalization and their role in training stability and optimization. Discuss attention variants and efficiency improvements such as sparse attention local windowed attention linear attention kernel based approximations and other methods to reduce memory and compute cost along with their trade offs. At senior and staff levels be prepared to reason about scaling Transformers to very large parameter counts including distributed training strategies parameter and data parallelism memory management and attention pattern design for long sequences and efficient inference. Be ready to apply this knowledge to sequence modeling language modeling and sequence transduction tasks and to justify architectural and implementation trade offs.

MediumTechnical

24 practiced

Compare and contrast attention efficiency improvements: Linformer, Performer, Reformer, Longformer, BigBird, and Nyströmformer. For each method summarize the key idea, computational and memory complexity, primary trade-offs, and the types of sequence tasks they are best suited for.

MediumTechnical

29 practiced

You need to implement rotary positional embeddings (RoPE) in a Transformer training pipeline using PyTorch. Describe the mathematical operation, exactly how you apply RoPE to Q and K tensors (shapes and index mapping), how to implement it efficiently for batched tensors, and how to handle inference when sequences are longer than training sequences.

MediumTechnical

32 practiced

Explain how to implement relative positional encodings in attention (for example, Shaw et al. or T5-style relative biases). Provide pseudocode showing how relative distances are converted to bias terms and added to attention logits, and discuss memory implications for long sequences and clipping strategies.

HardSystem Design

32 practiced

You are responsible for a deployed Transformer-based content classifier. Detail a monitoring and alerting plan to detect concept drift, data drift, performance degradation, and bias shifts. Include the metrics to monitor, statistical tests or thresholds to use, sampling and labeling strategy for drift alerts, and policies for automated or human-in-the-loop retraining.

HardTechnical

24 practiced

You are asked to implement a custom CUDA kernel for streaming attention that computes attention for long sequences with O(n) memory by processing chunks and accumulating KV summaries. Describe the algorithm, data layout, synchronization points, numerical stability considerations (for example, stable softmax across chunks), and how you would test correctness and measure performance relative to a matmul-based reference implementation.

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