Data Architecture and Pipelines Questions
Designing data storage, integration, and processing architectures. Topics include relational and NoSQL database design, indexing and query optimization, replication and sharding strategies, data warehousing and dimensional modeling, ETL and ELT patterns, batch and streaming ingestion, processing frameworks, feature stores, archival and retention strategies, and trade offs for scale and latency in large data systems.
HardSystem Design
0 practiced
Architect a multi-tenant ML data platform that supports tenant isolation, per-tenant quotas on storage/compute, dataset-level access controls, and cost attribution. Describe how namespaces, authentication/authorization, RBAC, resource enforcement (e.g., Kubernetes quotas), and billing metering would be implemented. Include ideas for cross-tenant dataset sharing and auditing.
MediumTechnical
0 practiced
Design a reproducible dataset and feature versioning strategy using tools like DVC, Delta Lake, or LakeFS. Explain how dataset snapshots, feature materializations, and commit hashes are recorded and how those link to specific model training experiments to enable exact reproduction of any historical model.
MediumTechnical
0 practiced
Create a pre-training dataset validation checklist for a dataset before it's allowed to be used to train a production model. Include steps for schema validation, label quality checks, distribution and missing value checks, bias and fairness tests, and required documentation/artifacts for sign-off.
HardSystem Design
0 practiced
Design a unified metadata catalog for an AI organization that lets users search datasets, features, experiments, and models. Describe APIs, indexing strategy, security model (RBAC), data ingestion pipelines for metadata, and how you would model relationships (lineage) so that queries like 'which datasets were used to produce feature X' are efficient.
HardSystem Design
0 practiced
Design a windowing and watermark strategy in Apache Flink or Beam to sessionize user events from a Kafka topic that contains out-of-order and late events. Your design should balance latency and completeness (allowed lateness), use side outputs for very late events, and explain how to reconcile updates to session aggregates when late events arrive.
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