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.
MediumTechnical
0 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.
EasyBehavioral
0 practiced
Tell me about a time when you had to convince product and engineering leadership to adopt a multi-year technical direction for AI infrastructure (for example, moving to model-serving microservices, investing in GPU clusters, or standardizing on a model platform). Describe the situation, alternatives considered, long-term trade-offs you evaluated, how you built and presented your strategic case, and the outcome.
EasyTechnical
0 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.
EasyTechnical
0 practiced
Describe what a canary deployment looks like for ML model updates (weights, preprocessing changes, or feature schema changes). Outline a rollout plan including traffic split schedule, metrics to monitor (latency, error rate, prediction distribution shift, business KPIs), automated rollback triggers, and how to validate model correctness on a small subset of traffic before full rollout.
HardTechnical
0 practiced
You're responsible for migrating a monolithic, on-prem inference system to a cloud-native microservices architecture across multiple products over a three-year horizon. Outline a phased migration plan (discovery, pilot, incremental migration, cutover), identify key technical risks, estimate KPIs to measure progress, and propose rollback and continuity plans for production traffic during migration.
Unlock Full Question Bank
Get access to hundreds of Strategic Technical Decision Making interview questions and detailed answers.
Sign in to ContinueJoin thousands of developers preparing for their dream job.