During deep learning and AI engineering interviews in 2026, recruiters test your deep comprehension of tensor graph computation, PyTorch autograd engine internals, custom loss formulations, and distributed GPU training optimizations. Here are 50 comprehensive questions and answers.
1. How does the PyTorch Dynamic Computational Graph (Define-by-Run) work compared to static graph frameworks?
In PyTorch, computation graphs are constructed dynamically on-the-fly during the forward pass. Every operation on a tensor with requires_grad=True appends an operation node to a Directed Acyclic Graph (DAG) whose leaf nodes are tensors and non-leaf nodes are Function objects. When backward() is invoked, PyTorch traverses this dynamic graph in reverse using the chain rule.
2. Why is optimizer.zero_grad() mandatory before calling loss.backward()?
By default in PyTorch, gradients accumulate (i.e. are summed) in the .grad attributes of parameters on successive backward() calls. While gradient accumulation is valuable for simulating large mini-batch sizes when GPU VRAM is restricted, failure to call optimizer.zero_grad() in standard training loops results in corrupted, compounding gradient vectors.
3. What is the difference between model.eval() and torch.no_grad()?
model.eval(): Changes the behavior of specific layers such as nn.Dropout (deactivating random neuron zeroing) and nn.BatchNorm2d (using running mean/variance instead of batch statistics). It does NOT disable graph construction or save memory.
torch.no_grad(): A context manager that disables autograd tracking entirely, reducing GPU memory consumption and speeding up inference.
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