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.

EasyTechnical

0 practiced

Compare absolute sinusoidal positional encodings, learned absolute positional embeddings, and relative positional encodings. Explain the advantages and disadvantages of each for language modeling and sequence-to-sequence tasks. Briefly describe rotary positional embeddings (RoPE) and when they are preferable.

HardTechnical

0 practiced

For a document retrieval and summarization pipeline, analyze trade-offs of replacing dense attention with BigBird sparse attention. Consider recall of distant dependencies, training convergence, ability to fine-tune from existing dense checkpoints, inference speed and memory. Provide a recommendation and fallback strategies if sparsity degrades quality.

HardTechnical

0 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.

HardSystem Design

0 practiced

Propose a design to shard a quantized 70B-parameter Transformer model across heterogeneous devices (multiple GPUs and CPU nodes) to serve high-throughput, low-latency requests. Address scheduling of requests, memory placement of shards, communication minimization, token-level latency optimization, and failover strategies.

MediumTechnical

0 practiced

Explain the differences between data parallelism, tensor (model) parallelism, pipeline parallelism, and ZeRO optimizer partitioning for training large Transformers. For each method describe communication patterns, memory savings, complexity, and scenarios when you would use it.

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