Strategic Technical Decision Making Questions
Focuses on higher level, organization impacting technical decisions and direction setting. Candidates should discuss evaluating long term implications, aligning technology choices with company strategy, managing uncertainty in multi year decisions, balancing innovation with operational risk, and communicating strategic rationale to leadership and across teams. Examples should show decisions that affected architecture, platform direction, or major product technical choices.
EasyTechnical
34 practiced
As an AI Engineer, outline high-level criteria you would use to choose between GPUs, TPUs, FPGAs, or CPU-based inference and training. Consider model size, batch size, latency/throughput targets, software ecosystem, development velocity, energy/power profile, and cost. Provide a short decision checklist for choosing hardware for training and separate checklist for inference.
MediumSystem Design
39 practiced
Design a resilient inference system that can tolerate partial regional outages and degraded upstream dependencies, minimizing user impact. Include fallback strategies (local cached models, redirecting to other regions, degraded feature sets), health-checking, circuit breakers, and how to coordinate failovers to avoid cascading overloads.
EasyTechnical
41 practiced
Explain best practices for model versioning and lineage in production: immutable model artifacts, metadata capture (hyperparameters, training data snapshot, evaluation metrics), schema contracts for inputs/outputs, promotion flow from staging to production, and how to support quick rollbacks and reproducibility across environments.
MediumSystem Design
35 practiced
Design a hybrid-cloud strategy for training large AI models where sensitive datasets must remain on-premise but occasional burst capacity is required in public cloud. Discuss data governance, secure data transfer or remote training, orchestration patterns, cost implications, and how to measure when to burst versus invest in on-prem capacity.
MediumTechnical
41 practiced
Design testing and CI/CD practices for an ML training and serving pipeline to ensure reproducible releases and secure deployment. Cover unit/integration tests for feature transforms, validation gates for model quality, artifact promotion, automated canaries, data and model drift tests, and how to integrate governance checks in pipelines.
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