AI and Machine Learning Background Questions
A synopsis of applied artificial intelligence and machine learning experience including models, frameworks, and pipelines used, datasets and scale, production deployment experience, evaluation metrics, and measurable business outcomes. Candidates should describe specific projects, roles played, research versus production distinctions, and technical choices and trade offs.
EasyTechnical
81 practiced
Explain what a feature store is and why a Solutions Architect should recommend it for production ML. Describe differences between online and offline features, consistency guarantees, serving APIs, expected latency, and give a concrete example (e.g., real-time fraud scoring). Mention data versioning and lineage briefly.
MediumTechnical
77 practiced
Explain the key differences between research (experimental) ML work and production ML from a Solutions Architect's perspective. Focus on reproducibility, data quality and lineage, performance guarantees, scalability, monitoring needs, and how teams need to shift responsibilities when moving from prototype to production. Provide examples of common pitfalls.
MediumTechnical
83 practiced
A customer trains large Transformer models weekly on a 50TB dataset. As a Solutions Architect, outline strategies to reduce cloud training cost while maintaining model quality: using spot/preemptible instances, mixed-precision training, gradient checkpointing, data sharding strategies, selective dataset sampling, and controlled hyperparameter search. Discuss the trade-offs for each.
MediumTechnical
67 practiced
Describe at least two statistical methods to detect feature distribution drift in production such as KL divergence, Population Stability Index (PSI), or Kolmogorov–Smirnov test. Explain how you'd choose thresholds, handle categorical features, and integrate these checks into continuous alerting without creating excessive false positives.
HardSystem Design
81 practiced
Design a multi-region inference architecture for an image classification API serving global users with 10M requests/day, <100ms P95 latency, and GDPR-compliant data residency requirements. Describe data flows, model synchronization approach, handling of stale models, consistency models, caching strategy, and rollback procedures across regions.
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