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.
HardSystem Design
0 practiced
Design a CI/CD pipeline for ML models that includes automated data validation tests, unit and integration tests for featurization, model validation tests (performance and fairness), artifact promotion, canary rollout to 1% of traffic, automatic rollback criteria, and BI gating. Describe tools and gating thresholds you would use.
MediumTechnical
0 practiced
Write a Postgres/Redshift-compatible SQL query to compute a calibration table that buckets predicted probabilities into deciles per day. Use schema: predictions(prediction_id bigint, user_id bigint, predicted_prob numeric, predicted_at timestamp, label boolean). Output columns: date, bucket (0.0-0.1, ...), mean_pred_prob, observed_positive_rate, count.
HardSystem Design
0 practiced
Architect an end-to-end ML platform that serves models and integrates outputs into organization-wide BI dashboards. Requirements: support 100 models, 1B predictions/day, multi-tenant access, model versioning, monitoring, explainability, role-based access, and two-year retention. Describe components, data flows, choices for model store and feature store, batch vs streaming, and how BI will consume metrics.
HardTechnical
0 practiced
Design an approach to monitor fairness metrics across demographic subpopulations for a lending model in production. Define which fairness metrics to compute (for example, disparate impact and equal opportunity), sampling strategies for small groups to get stable estimates, and how to display and alert on violations in BI tools.
MediumSystem Design
0 practiced
Design a data flow to push model predictions every five minutes into a Power BI dashboard that supports ad-hoc filters and historical trend analysis for 50 million users. Specify components (streaming source, message bus, storage, aggregation layer), expected end-to-end latency, schema considerations, and failure/retry behavior.
Unlock Full Question Bank
Get access to hundreds of AI and Machine Learning Background interview questions and detailed answers.
Sign in to ContinueJoin thousands of developers preparing for their dream job.