A regulator asks you to show exactly how a credit decision model was built and how a past decision was made. How should your MLOps setup support this?

What the interviewer wants to hear

• End-to-end lineage: data, code, config, model version
• Immutable artifacts and audit logs
• Prediction logs with model version and inputs
• Documentation: model cards, validation and approvals
• Access control, retention and privacy rules

Model answer

The platform should make this a lookup, not an investigation. Every model version in the registry links to the exact training data snapshot, feature definitions, code commit, configuration, environment and evaluation results, and these artifacts are immutable. Each version also has documentation such as a model card, validation report, fairness checks and the names of people who approved it, with timestamps. For individual decisions, the serving layer logs a request ID, the model version, the input features used and the output, plus explanation values if required, stored securely with retention rules that match the regulation. Access to these logs is controlled and itself audited. With this, I can answer which model and data produced a decision on a given date, reproduce the prediction, and show the approval trail.

More hard MLOps Engineer questions

• Design an internal ML platform for 15 data science teams who currently deploy models in different ad hoc ways.
• How would you self-host and serve an open-weight LLM for thousands of concurrent users?
• Your recommendation model suddenly shows a 25% drop in click-through rate. Walk through your incident response.
• The ML infrastructure bill doubled in six months. How do you bring it down without slowing teams?
• You need to run a multi-day, multi-node training job on preemptible GPUs. How do you make it reliable and cost-effective?
• Your RAG system has 200 million chunks indexed with an old embedding model. How do you move to a new embedding model without downtime or quality loss?
• Users say a self-hosted LLM feature is too slow. How do you reduce latency without destroying throughput or quality?
• Under privacy law, a customer asks you to delete all of their data. How should an ML platform handle this across datasets, features, indexes and models?
• How do you build a real-time feature pipeline whose values match the batch features used in training?