This was the one I actually felt okay about because I had a real project to pull from.
Choose a concrete project where you fine-tuned an LLM for a specific task, and structure your answer as a narrative that covers each sub-question in order. Emphasize the trade-offs you made (e.g., LoRA vs full fine-tuning, loss function choice, evaluation metrics) and how you addressed deployment constraints and hallucinations. Keep the story focused on your decision-making process and measurable outcomes.
Pro tip: Quantify the impact of your fine-tuning (e.g., 'reduced hallucination rate by 30%' or 'improved F1 by 15 points') and mention how you validated the model against a holdout set and in production. Also, show awareness of Snapchat's scale and latency constraints by discussing how you optimized inference (e.g., quantization, distillation).
Clearly state the problem (e.g., content moderation, caption generation) and how you built and labeled the dataset, including sourcing, annotation guidelines, and quality checks.
Explain whether you used full fine-tuning or a parameter-efficient method (e.g., LoRA, prefix tuning) and why, and describe the loss function (e.g., cross-entropy, contrastive) and any regularization.
List evaluation metrics (e.g., accuracy, F1, BLEU, human eval) and how you detected and addressed overfitting (e.g., early stopping, dropout) and hallucinations (e.g., retrieval augmentation, constrained decoding).
Discuss deployment constraints (latency, memory, cost) and how you optimized the model (e.g., quantization, caching) and set up monitoring for performance and drift.
Summarize key trade-offs (e.g., accuracy vs. latency, cost vs. performance) and what you would do differently next time.
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Felt like a warmup but they wanted more than a textbook rundown.
Start by defining regularization as any technique that reduces generalization error by constraining the model, then group the methods into parameter-level (L1, L2, weight decay), architectural (dropout), and training-level (early stopping, data augmentation). For each, briefly explain the mechanism, the trade-off it introduces, and when it is most effective, using a consistent structure to compare them.
Pro tip: Emphasize that weight decay and L2 are not always identical—only for SGD without momentum; with Adam, decoupled weight decay (AdamW) is preferred. Mentioning this nuance shows depth and practical experience.
Explain that regularization adds constraints or noise to reduce overfitting and improve generalization, trading off training accuracy for test performance.
Compare L1 (sparsity, feature selection), L2 (weight shrinkage, smoothness), and weight decay (often equivalent to L2 but can be decoupled).
Describe dropout as randomly dropping units during training to prevent co-adaptation, acting like an ensemble.
Cover early stopping (halting when validation error rises) and data augmentation (increasing effective data diversity).
Summarize trade-offs: computational cost, hyperparameter sensitivity, and suitability for different data sizes and model types.
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Start by briefly defining each optimizer's core update rule, then compare them along axes like convergence speed, memory overhead, and generalization. Finally, give concrete scenarios for when you'd pick each, tying choices to model architecture, dataset size, and training constraints.
Pro tip: Mention that AdamW's decoupled weight decay often yields better generalization than Adam with L2 regularization, and that for large-scale production models at Snapchat, AdamW is a safe default unless you have a specific reason to use something else.
Briefly state the update rule for SGD, SGD with momentum, Adam, and AdamW, highlighting the key difference: momentum adds velocity, Adam uses adaptive per-parameter learning rates with bias correction, and AdamW decouples weight decay from the gradient update.
Compare them on convergence speed, memory footprint, hyperparameter sensitivity, and generalization. For example, SGD with momentum can generalize better but requires tuning; Adam converges faster but may overfit; AdamW improves regularization.
Explain when to pick each: SGD with momentum for well-tuned CNNs on large datasets; Adam for quick prototyping or sparse gradients; AdamW for transformers and when weight decay matters; plain SGD rarely used except as a baseline.
Discuss how factors like model size, batch size, and available compute influence the choice. For instance, Adam's memory overhead may be prohibitive for huge models, while SGD with momentum is more memory-efficient.
Conclude with a simple heuristic: start with AdamW for most deep learning tasks, switch to SGD with momentum if you need better generalization and can afford tuning, and use Adam for rapid experimentation.
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I wrote out the query-key-value formulation and the scaled dot-product softmax.
Start by explaining the intuition behind self-attention as a mechanism for contextualizing each token using all others, then derive the key equations for scaled dot-product attention. Extend to multi-head attention by describing how multiple attention heads capture diverse relationships, and provide the corresponding equations.
Pro tip: Emphasize the computational complexity and trade-offs of self-attention (e.g., O(n^2) memory) and how multi-head attention improves representational capacity without significantly increasing parameters, showing awareness of practical deployment constraints.
Explain why self-attention is needed: to model long-range dependencies and context dynamically, unlike fixed convolutions or recurrent layers.
Present the core equation: Attention(Q, K, V) = softmax(QK^T / sqrt(d_k)) V, and explain each component (queries, keys, values, scaling).
Describe how multiple heads project Q, K, V into lower-dimensional subspaces, apply attention in parallel, and concatenate outputs followed by a linear projection.
Write the equations: head_i = Attention(Q W_i^Q, K W_i^K, V W_i^V), MultiHead(Q, K, V) = Concat(head_1, ..., head_h) W^O.
Highlight how multi-head attention captures diverse patterns and improves performance, while noting increased computational cost and the need for efficient implementations.
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Honestly the most stressful one because they wanted actual math, not just a diagram description.
Start with a high-level overview of the Transformer architecture, then zoom into the decoder block and walk through the mathematical operations step by step. Use clear notation and explain the purpose of each component, connecting them to the overall goal of sequence generation.
Pro tip: Emphasize how the decoder's masked self-attention and cross-attention enable autoregressive generation and conditioning on encoder outputs, which is crucial for tasks like machine translation and text generation. Mention that this design allows parallel training while maintaining sequential inference.
Briefly describe the Transformer as an encoder-decoder model with stacked layers, highlighting the key components: multi-head attention, feed-forward networks, residual connections, and layer normalization. Mention positional encodings to inject sequence order.
Explain that each decoder layer has three sub-layers: masked multi-head self-attention, multi-head cross-attention over encoder outputs, and a position-wise feed-forward network. Each sub-layer is followed by residual connection and layer normalization.
Detail the computation: given input X, compute queries Q = XW_Q, keys K = XW_K, values V = XW_V. Compute attention scores S = QK^T / sqrt(d_k), apply a causal mask (set future positions to -inf), then softmax to get attention weights A = softmax(S). Output is A V. For multi-head, split into h heads, compute in parallel, concatenate, and project with W_O.
For cross-attention: queries come from the previous decoder sub-layer output, while keys and values come from the encoder output. Compute similarly: Q = YW_Q, K = ZW_K, V = ZW_V, where Y is decoder hidden state and Z is encoder output. Then compute attention and output. For feed-forward: apply two linear transformations with a ReLU in between: FFN(x) = max(0, xW_1 + b_1)W_2 + b_2.
After each sub-layer, apply residual connection and layer normalization: output = LayerNorm(x + Sublayer(x)). This stabilizes training and helps gradients flow. Finally, the decoder output is passed through a linear layer and softmax to produce probabilities over the vocabulary.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.