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

0 practiced

You need to support real-time inference on documents of 100k tokens with acceptable latency. Propose attention patterns (global tokens, sliding window, hierarchical summarization, chunking and downsampling) to reduce compute while preserving cross-chunk dependencies. Explain how to implement cross-chunk attention and how you would evaluate the quality loss introduced.

HardTechnical

0 practiced

Discuss potential vulnerabilities of Transformer-based systems to adversarial inputs and prompt-injection attacks during inference. Propose detection and mitigation strategies at the model, serving, and product layers that reduce risk while retaining usability and explain how you'd measure effectiveness.

HardSystem Design

0 practiced

Architect a training pipeline to train a 100B-parameter Transformer across multi-node GPU clusters. Cover data ingestion (sharding, streaming, deduplication), parallelism strategy (tensor + pipeline + data), optimizer state management (ZeRO), checkpointing, failure recovery, and cost/performance trade-offs. Explain choices to minimize wall-clock time while preserving numerical stability.

MediumTechnical

0 practiced

Outline best practices for fine-tuning large pre-trained Transformer models on a downstream classification task. Cover data preparation, learning rate schedules, layer freezing strategies, regularization, batch size choices, mixed-precision training, and validation strategies to avoid catastrophic forgetting.

MediumTechnical

0 practiced

You're training a 10B-parameter Transformer. Select an optimizer (AdamW, LAMB, Adafactor), a learning rate schedule (warmup, decay), and core hyperparameters (weight decay, betas). Justify your choices and explain how you'd tune them to stabilize large-batch distributed training.

Unlock Full Question Bank

Get access to hundreds of Transformer Architecture and Attention interview questions and detailed answers.

Join thousands of developers preparing for their dream job.