Covers both technical and organizational strategies for growing capacity, capability, and throughput. On the technical side this includes designing and evolving system architecture to handle increased traffic and data, performance tuning, partitioning and sharding, caching, capacity planning, observability and monitoring, automation, and managing technical debt and trade offs. On the organizational side this includes growing engineering headcount, hiring and onboarding practices, structuring teams and layers of ownership, splitting teams, introducing platform or shared services, improving engineering processes and effectiveness, mentoring and capability building, and aligning metrics and incentives. Candidates should be able to discuss concrete examples, metrics used to measure success, trade offs considered, timelines, coordination between product and infrastructure, and lessons learned.
HardTechnical
67 practiced
At scale, models can produce harmful or hallucinated outputs that damage user trust. Propose a production plan to detect and mitigate such catastrophic failures: monitoring signals, automated throttles, fallback policies (rule-based, cached responses), human-in-the-loop escalation, incident management, and post-incident analysis. Include how to balance false positives and negatives in safety detectors.
EasyTechnical
50 practiced
At a high level, explain data parallelism versus model parallelism for training deep neural networks. Give example scenarios where each is appropriate (including hybrid approaches), and list the operational trade-offs (communication overhead, batch-size limits, memory usage, framework support).
HardSystem Design
56 practiced
Design a model governance system comprising a model registry, approval workflows, model cards, audit logging, and automated checks before deployment. Describe enforcement points, role-based access, required metadata, and how to integrate this system into CI/CD so deployments are safe but not overly slow.
MediumTechnical
49 practiced
List and explain practical strategies to reduce inference cost for large models in production (quantization, knowledge distillation, pruning, batching, caching, dynamic routing). For each strategy, describe expected cost reduction ranges, impact on model quality, and operational complexities (e.g., retraining, validation, supported hardware).
HardSystem Design
48 practiced
Design a privacy-aware logging and observability approach for model inference that minimizes PII exposure but retains enough signal for debugging and monitoring. Include log redaction strategies, tokenization/anonymization, differential privacy considerations, retention policies, and how to enable safe replay for debugging while complying with regulations.
Unlock Full Question Bank
Get access to hundreds of Scaling Systems and Teams interview questions and detailed answers.
