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.
HardTechnical
0 practiced
Your company must honor GDPR 'right to be forgotten' and delete all PII related to certain users across a large data lake, feature store, model artifacts, and backups. Design processes and systems to identify all PII locations, remove or anonymize it across derived data and models, verify deletion, and handle immutable backups while minimizing service disruption.
HardSystem Design
0 practiced
Design a versioned feature lineage and experiment tracking system that lets a data scientist reproduce any past model run including the exact feature computation DAG, code commit, dataset snapshot, and runtime environment. Describe metadata storage, retrieval APIs, UI/UX considerations, and how to balance storage cost versus reproducibility fidelity.
MediumTechnical
0 practiced
You're collaborating with data engineering to validate a Kafka-based event stream contract before consuming it for a feature pipeline. How would you validate event schema compatibility, produce a test harness to simulate production load, and integrate contract tests into CI so the consumers detect incompatible changes early?
MediumTechnical
0 practiced
Given a Parquet-based training dataset on S3, design an efficient incremental backfill process to recompute features for historical partitions without reprocessing the entire dataset every run. Explain how you would track incremental markers, perform partition pruning, make the pipeline idempotent, and avoid inconsistent partial writes.
EasyTechnical
0 practiced
Explain the difference between OLTP and OLAP systems. As a data scientist, which would you query for exploratory data analysis and why? Discuss when row-oriented storage makes sense versus columnar storage and how that affects model feature extraction.
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