TensorFlow/PyTorch Framework Fundamentals Questions
Practical knowledge of a major deep learning framework. Includes understanding tensors, operations, building neural network layers, constructing models, and training loops. Ability to read and modify existing code in these frameworks. Knowledge of how to work with pre-built layers and models.
HardSystem Design
0 practiced
Design an observability and monitoring plan for long-running training jobs. Include which metrics to expose (losses, per-class metrics, learning rate, GPU utilization, memory, data pipeline throughput), logging backends (TensorBoard, Weights & Biases, Prometheus), alerting rules, and how to capture and store model artifacts and reproducibility metadata.
EasyTechnical
0 practiced
Explain how automatic differentiation works in PyTorch (autograd). In your answer, include: what requires_grad means, how the dynamic computation graph is built and used during backward(), and a short example where you compute gradients of y = 3*x^2 + 2*x for x being a tensor with requires_grad=True.
HardTechnical
0 practiced
Explain activation (gradient) checkpointing and show how to use torch.utils.checkpoint to trade extra computation for lower GPU memory usage when training very deep models. Include a minimal code example wrapping a sequential stack and discuss limitations and recomputation overhead.
MediumTechnical
0 practiced
Describe best practices for saving and restoring model checkpoints in PyTorch and TensorFlow so that training can be resumed exactly. Include what to save (model weights, optimizer state, scheduler state, training step, RNG states), sample code snippets for both frameworks, and tips for moving checkpoints between devices (CPU/GPU).
HardSystem Design
0 practiced
Design how to shard and stream a very large dataset stored in cloud object storage (S3/GCS) for multi-node distributed training. Discuss file formats (TFRecord, LMDB, Parquet), how to ensure each worker sees a distinct shard without overlap, caching strategies, and handling eventual consistency and network variability.
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