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.
MediumSystem Design
0 practiced
Design a production ML architecture for an online recommendation service that must serve 100M requests per day with 50ms P95 latency. Outline components for feature storage, model serving, caching layers, model update pipeline, monitoring/observability, and fault tolerance. State assumptions about dataset size, model size, and expected throughput per server.
MediumSystem Design
0 practiced
How would you architect explainability and auditability for ML models used in regulated industries (finance, healthcare)? Describe logging and storage of explanations, explainability tool integration, deterministic reproducibility, stakeholder-facing explanation formats, and how to meet audit requests for decision rationale.
MediumTechnical
0 practiced
Recommend storage architectures for large-scale ML training data and artifacts (50TB+). Compare options: object storage (S3), HDFS, Parquet/columnar formats with partitioning, and data versioning tools (DVC, Delta Lake). Address throughput, cost, random-read patterns, compatibility with distributed training, and reproducibility.
HardSystem Design
0 practiced
Compare blue-green and canary deployment strategies for ML models. Design a deployment pipeline that supports automated canary analysis for a new model version: traffic splitting, metric collection and automated statistical checks, rollback automation, and safety nets to prevent harmful degradations. Include considerations for stateful services and data-related rollouts.
MediumTechnical
0 practiced
Compare and contrast batch training vs streaming (online) training in production. For a client with rapidly changing user behavior (e.g., trending items), recommend which approach to use, describe system implications (latency, convergence guarantees, resource needs), and outline how you'd implement streaming updates safely (feature consistency, checkpointing, validation).
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