End to End Machine Learning Problem Solving Questions
Assesses the ability to run a complete machine learning workflow from problem definition through deployment and iteration. Key areas include understanding the business or research question, exploratory data analysis, data cleaning and preprocessing, feature engineering, model selection and training, evaluation and validation techniques, cross validation and experiment design, avoiding pitfalls such as data leakage and bias, tuning and iteration, production deployment considerations, monitoring and model maintenance, and knowing when to revisit earlier steps. Interviewers look for systematic thinking about metrics, reproducibility, collaboration with data engineering teams, and practical trade offs between model complexity and operational constraints.
HardSystem Design
26 practiced
For a recommendation model that benefits from batching queries, explain the design choices when trading off throughput versus latency: dynamic batching, tuning batch size, model partitioning, multi-tenant GPU scheduling, inference caching, and techniques to measure and optimize 95th-percentile latency while maintaining high throughput.
HardTechnical
31 practiced
Design a distributed training strategy to train a 10-billion-parameter transformer model. Discuss and compare data parallelism, tensor/model parallelism, pipeline parallelism, optimizer-state sharding (e.g., ZeRO), gradient accumulation, mixed-precision training, checkpointing strategies (frequency, incremental checkpoints), and approaches for failure recovery and resuming training.
MediumTechnical
35 practiced
Describe approaches to reduce model size and latency for mobile deployment: weight pruning, post-training quantization and quantization-aware training, knowledge distillation, efficient architecture choices (MobileNet, EfficientNet-lite), and runtime accelerators (ONNX, TFLite). For each method describe expected impact on accuracy and inference speed and how you'd validate on target hardware.
HardTechnical
50 practiced
You are the AI Engineer coordinating a regulated ML project that requires data from multiple teams (product, infra, legal, data engineering). Describe how you would define and enforce data contracts (schemas, freshness, ownership), set responsibilities and SLAs, build a roadmap that balances compliance and delivery speed, manage stakeholder communications, and establish maintenance and incident response procedures for models in production.
HardTechnical
30 practiced
Design an experiment and modeling approach to estimate individualized treatment effects (uplift) for a marketing campaign. Cover randomized trial design vs observational approaches, uplift model families (T-learner, S-learner, X-learner, causal forests), evaluation metrics (AUUC, Qini), and validation strategies to ensure unbiased uplift estimates.
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