Interview Prep
LLM Engineer Interview Questions
LLM and transformer questions now show up in general data science and ML engineering loops, not just specialized AI roles. Practice the exact concepts interviewers ask about in 2026 — attention, LoRA, RAG, quantization — with explanations of what a strong answer covers.
This hub is for data scientists, ML engineers, and applied AI candidates who need to get fluent in LLM internals before an interview. It covers four areas hiring teams actually probe: how transformers and attention work under the hood, the neural network fundamentals that underpin them, the NLP concepts that predate and still inform LLM pipelines, and the deep learning frameworks (PyTorch, TensorFlow) used to build and fine-tune models. Every question below is a real practice problem — short conceptual checks as well as deeper multi-part questions — with a written explanation of what interviewers are listening for, not just a one-line answer. 9 of these are free to try right now, no signup required.
LLM Fundamentals
- BERT vs GPT Pre-training Easy
- Purpose of Positional Encoding Easy
- Temperature in Text Generation Free Easy
- Top-k and Top-p Sampling Easy
- What Is a Token in LLMs? Free Easy
- What Is BPE Tokenization? Easy
- What Is Model Hallucination? Easy
- What Is RAG? Easy
- Word2Vec vs Contextual Embeddings Easy
- Zero-Shot vs Few-Shot Learning Free Easy
- AI Alignment Challenges Hard
- BLEU and ROUGE Metrics Hard
- Constitutional AI Hard
- Flash Attention Hard
- KV Cache in Autoregressive Generation Hard
- Mixture of Experts Architecture Hard
- Scaling Laws and Chinchilla Hard
- SentencePiece vs WordPiece Tokenization Hard
- Speculative Decoding Hard
- Transformer Architecture Deep Dive Hard
- Chain-of-Thought Prompting Medium
- Chunking Strategies for RAG Medium
- Context Window Limitations Medium
- DPO vs RLHF Medium
- Emergent Abilities in LLMs Medium
- Instruction Tuning Medium
- LoRA Fine-Tuning Medium
- Model Distillation Medium
- Multi-Head Attention Medium
- Perplexity as an Evaluation Metric Medium
- Prompt Engineering Techniques Medium
- Quantization for LLM Inference Medium
- RLHF Training Pipeline Medium
- Self-Attention Mechanism Medium
- Vector Databases for RAG Medium
Transformers & Neural Networks
- Backpropagation Fundamentals Free Easy
- CNN Pooling Layers Easy
- Cross-Entropy Loss Easy
- Data Augmentation Purpose Easy
- Perceptron Limitations Easy
- Purpose of Dropout Free Easy
- ReLU Activation Function Free Easy
- Softmax Output Layer Easy
- Transfer Learning Benefit Easy
- Vanishing Gradient Problem Easy
- AdaGrad Limitations Hard
- Cosine Annealing Schedule Hard
- Depthwise Separable Convolutions Hard
- Encoder-Decoder and U-Net Architecture Hard
- Exploding Gradients and Gradient Clipping Hard
- Fine-Tuning vs Feature Extraction Hard
- GAN Training Dynamics Hard
- GELU and Leaky ReLU Activations Hard
- Hinge Loss for SVMs vs Neural Networks Hard
- Variational Autoencoder Latent Space Hard
- Adam Optimizer Internals Medium
- Attention Mechanism in Seq2Seq Medium
- Batch Normalization Mechanism Medium
- Convolution Stride and Output Size Medium
- Early Stopping Strategy Medium
- GRU vs LSTM Medium
- He Initialization for ReLU Medium
- Layer Normalization vs Batch Normalization Medium
- Learning Rate Warmup Medium
- LSTM Gating Mechanism Medium
- Multi-Head Attention Medium
- Positional Encoding in Transformers Medium
- Self-Attention in Transformers Medium
- Skip Connections in ResNets Medium
- Xavier Weight Initialization Medium
NLP
- Bag of Words Representation Easy
- Regular Expressions in NLP Easy
- Stemming vs Lemmatization Free Easy
- Text Normalization Steps Easy
- TF-IDF Intuition Easy
- What Are N-grams? Easy
- What Are Stopwords? Free Easy
- Coreference Resolution Hard
- Dependency Parsing Hard
- Edit Distance Applications Hard
- Information Extraction Pipelines Hard
- Machine Translation Challenges Hard
- Word Sense Disambiguation Hard
- Beam Search Decoding Medium
- BLEU Score Medium
- Document Similarity with Cosine Medium
- Extractive vs Abstractive Summarization Medium
- Named Entity Recognition Medium
- POS Tagging Purpose Medium
- Sentiment Analysis Approaches Medium
- Seq2Seq Architecture Medium
- Text Classification Pipeline Medium
- Topic Modeling with LDA Medium
- Word Embeddings vs One-Hot Encoding Medium
Deep Learning Frameworks
- DataLoaders in PyTorch Easy
- GPU Training Basics Easy
- Model Serialization Easy
- PyTorch vs TensorFlow Free Easy
- TensorBoard Usage Easy
- MLflow and Experiment Tracking Hard
- Model Serving: TorchServe and TF Serving Hard
- Scikit-learn Pipelines Hard
- Distributed Training: DDP Medium
- Dynamic vs Static Computation Graphs Medium
- Gradient Accumulation Medium
- Hugging Face Transformers Library Medium
- Mixed Precision Training Medium
- Model Checkpointing Medium
- ONNX Format Medium
Guides
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