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Domain and Product Technical Knowledge Questions

Evaluation of deep, domain specific technical knowledge relevant to the team, product, or role. Candidates should demonstrate subject matter expertise in the relevant problem space and be able to explain core concepts, architectures, algorithms, and practical engineering trade offs. Example domains include recommendation systems, data platform engineering, security, and analytics, as well as platform areas such as application programming interface platform management, developer experience, deployment orchestration, infrastructure and reliability, and observability. Expect questions on domain specific algorithms, data pipelines, real time versus batch trade offs, feature stores, data governance, versioning strategies, integration patterns, common customer use cases, and typical product pain points. For product focused roles, be prepared to explain core product features, typical customer workflows, integration points, and how domain constraints influence product decisions. For role or platform focused discussions, describe how the domain shapes responsibilities, challenges, and priorities and outline approaches to initial discovery, diagnosis, and early improvements. This topic tests both conceptual depth and the ability to map domain knowledge to concrete product and engineering decisions.

MediumTechnical
0 practiced
Describe the role of a data engineer in the MLOps lifecycle, focusing on dataset preparation, feature pipelines, model serving support, drift detection instrumentation, and retraining automation. Provide concrete deliverables and success metrics that a data engineer should own.
MediumTechnical
0 practiced
Outline a rollout plan for adopting a centralized schema registry and metadata catalog across multiple teams. Include pilot selection, migration steps, compatibility policies, CI integration, enforcement mechanisms, training, and measures to minimize breaking changes and developer friction.
EasyTechnical
0 practiced
Explain what a feature store is, where it sits in the ML lifecycle, and what responsibilities a data engineer has when implementing it. Cover offline feature computation, online serving, latency requirements, feature versioning, and how to prevent training-serving skew in production.
EasyTechnical
0 practiced
Describe Change Data Capture (CDC): the common approaches, trade-offs between log-based and trigger-based implementations, how CDC integrates into streaming ingestion pipelines, and typical pitfalls such as schema changes, ordering, and transactional boundary issues.
EasyTechnical
0 practiced
What SLOs and SLAs would you propose for core data pipelines that feed analytics dashboards and ML models? Include key metrics such as freshness, completeness, and accuracy, how to measure them, examples of error budgets, and an escalation policy when SLOs are breached.

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