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.
MediumTechnical
0 practiced
Given a predictions table with schema (user_id, actual_label INT, predicted_label INT, predicted_prob FLOAT, predicted_at TIMESTAMP), write a Postgres SQL query to compute daily precision, recall, and support per class for the last 30 days, grouped by date and predicted_label. Include handling for days with zero samples to report zeros.
MediumTechnical
0 practiced
As a Solutions Architect, how would you design an A/B testing framework for evaluating a new ranking model in production? Include traffic allocation strategies, business and model metrics to track, experiment duration and statistical significance calculation, guarding against novelty effects, and safe rollback criteria.
HardSystem Design
0 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.
HardTechnical
0 practiced
Compare fine-tuning a large language model versus using retrieval-augmented generation (RAG) to add domain knowledge. Discuss costs, latency, maintainability, privacy and data residency, hallucination risk, update frequency, and operational complexity. As a Solutions Architect, provide recommendation criteria for when to choose each approach.
MediumTechnical
0 practiced
Given user activity events (user_id, event_type, value, timestamp) at 50M events/day, design the offline and online feature ingestion pipeline and schema for aggregations such as daily_active_minutes and avg_event_value_7d. Specify how you'd ensure freshness for online features, compute sliding windows, and handle late-arriving events and backfills.
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