Hands On Projects and Problem Solving Questions

Discussion of practical projects and side work you have built or contributed to across domains. Candidates should be prepared to explain their role, architecture and design decisions, services and libraries chosen, alternatives considered, trade offs made, challenges encountered, debugging and troubleshooting approaches, performance optimization, testing strategies, and lessons learned. This includes independent side projects, security labs and capture the flag practice, bug bounty work, coursework projects, and other hands on exercises. Interviewers may probe for how you identified requirements, prioritized tasks, collaborated with others, measured impact, and what you would do differently in hindsight.

EasyTechnical

68 practiced

Design a simple ETL pipeline to prepare image classification training data where labels are in a CSV and images are stored on cloud object storage. Include choices for orchestration (pandas, Dask, Beam, Airflow), transformations, sharding for training, validation checks, and how to produce reproducible dataset snapshots.

EasyTechnical

64 practiced

Write a minimal Dockerfile and outline a small FastAPI app in prose to serve a PyTorch model on CPU. Requirements: base image 'python:3.10-slim', install requirements from 'requirements.txt', copy model file 'model.pt', expose port 8000, and run uvicorn with a worker option. Explain how to build and run the container locally.

MediumTechnical

95 practiced

Your team will train a 1B parameter model monthly. Compare AWS p3/p4d, GCP A2, and Azure ND offerings in terms of GPU performance (FP32/FP16 throughput), memory capacity, interconnect bandwidth, availability, and cost. Which provider metrics would you collect to make the final decision?

HardSystem Design

68 practiced

Design a production-scale federated learning system where models are trained on mobile devices and aggregated centrally. Address secure aggregation, communication efficiency (compression), client selection and fairness, calibration of differential privacy noise, model validation under heterogeneity, and operational concerns like client dropout and versioning.

HardTechnical

64 practiced

Design an A/B testing pipeline for ML models that supports online metrics collection, sample size estimation, statistical significance testing, traffic routing, and automatic rollback when a safety metric degrades. Include how to define experiments, collect telemetry, and produce a clear decision rule for rollout or rollback.

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