Easy interview questions in AI & ML, page 5
GAN architecture: generator and discriminator roles
Generator maps noise to fake samples, discriminator classifies real versus fake, they train as a two-player game until samples fool the discriminator.
P-value vs confidence interval in an A/B test
A p-value gauges evidence against the null; a 95% CI shows plausible effect sizes and precision.
Red teaming LLMs and harmful output categories
Red teaming deliberately probes a model to elicit harmful behavior; categories include toxic or hateful content and dangerous instructions like weapons or malware.
Diffusion forward and reverse processes
Forward process gradually adds Gaussian noise until data is pure noise; reverse process learns to denoise step by step; the network predicts the noise added at each timestep.
A/B test: 0.1% lift. Statistical vs practical significance?
Statistical significance says the 0.1% is real; practical significance asks if revenue exceeds engineering cost. Frame with CIs and ROI.
Stages of an end-to-end ML pipeline
Ingest, validate, preprocess and feature engineer, train, evaluate against a baseline, then register and package for deployment, with gates between stages.
What triggers automatic full retraining in an ML pipeline?
A strong answer lists four triggers: fresh data, code changes, model drift, and scheduled cadence.
Model quantization benefits and risks
Quantization stores weights and activations in lower precision like INT8 or INT4; benefits are smaller memory and faster, cheaper inference; risk is accuracy loss.
How the KV cache speeds transformer generation
Cache stores past keys and values so each new token only computes its own K, Q, V instead of recomputing all prior tokens, cutting per-step cost from quadratic to linear.
Teacher-student knowledge distillation
A small student learns to mimic a large teacher's soft probability outputs, not just hard labels; goal is a compact model retaining most capability.
Explain vanishing and exploding gradients and common mitigation techniques.
Why deep backpropagation causes diverging gradient magnitudes. Repeated multiplication across layers shrinks or explodes gradients; cite tanh [0,1] range; list ReLU, batch norm, and gradient clipping. Blaming activation choice alone without citing depth.
What are the essential components of an end-to-end ML platform?
Tests mapping the ML lifecycle to infrastructure. A strong answer walks through six stages: data ingestion, feature store, training pipeline, model registry, deployment/serving, and monitoring with feedback loops.
What is overfitting and how does Dropout prevent it?
Tests generalization intuition: overfitting is low train error but high test error. Good answers say dropout randomly zeros hidden units during training to stop co-adaptation. Bad answers say dropout permanently deletes neurons or just reduces capacity.
Semantic, instance, and panoptic segmentation
Semantic labels every pixel by class without separating objects; instance separates individual objects but may skip background; panoptic unifies both, labeling stuff and distinct thing instances.
What is a feature store and how does it prevent training-serving skew?
This tests training-serving consistency via centralized feature management. Covers offline batch storage, online serving, shared transformations, and alternatives like ad-hoc ETL. A red flag is calling it just a database and ignoring point-in-time correctness.
What is a word embedding and how does it beat one-hot encoding?
Tests dense semantic vectors versus sparse one-hot representations. A good answer defines embeddings as learned real-valued vectors where similar words are close, contrasts them with orthogonal one-hot vectors lacking similarity, and names Word2Vec or GloVe.
Rule-based versus model-based LLM guardrails
A guardrail is a programmatic check constraining LLM I/O; rule-based uses regex or blocklists, model-based uses a classifier like a moderation model to detect harmful content.
Designing a baseline Visual Question Answering model
Encode the image with a CNN, encode the question with an RNN or embedding, fuse the two vectors, and classify over a fixed answer vocabulary.
Fine-tuning vs RAG for daily-updated docs
Choose RAG because docs change daily, embed and index chunks in a vector store, retrieve top matches and inject into the prompt.
Transfer learning from ResNet50 on small data
Replace the final classification head with one sized to your classes, freeze the pretrained convolutional backbone as a feature extractor, train the new head, then optionally fine-tune top blocks at a low…
We are hiring for this. Every open role lists the topics its interview covers, so you can prepare for the real thing rather than guessing.
See open roles