Cloud Data Architecture and Tradeoffs Questions

Designing data architectures specifically for cloud environments and evaluating platform trade offs. Topics include when to use managed relational services, managed nonrelational services, cloud data warehouses, cloud object storage, lifecycle policies, cross region replication, data residency and compliance considerations, cost versus performance trade offs, managed service operational constraints, and strategies for high availability and disaster recovery in the cloud. Candidates should be able to compare cloud service options and justify choices based on reliability, cost, and compliance.

MediumSystem Design

47 practiced

Design a high-level cloud architecture for a data lake used by analysts across multiple business units that must meet strict access controls, support ad-hoc queries, and optimize storage cost. Include choices for object storage, metadata/catalog, compute/query engines, and access control enforcement points. Assume AWS as the target cloud.

EasyTechnical

52 practiced

Describe three cloud-native strategies to reduce egress and data transfer costs for a global analytics platform that aggregates data from multiple regions into a central data lake.

MediumTechnical

63 practiced

You must choose between a serverless query service (e.g., Athena, BigQuery) and a provisioned cloud data warehouse (e.g., Redshift, Synapse) for a startup with unpredictable query workloads and limited budget. Compare the trade-offs in cost predictability, performance for large joins, concurrency, and operational burden.

HardSystem Design

57 practiced

You must design a multi-tenant analytics platform in the cloud where tenants share the same object storage but must be logically isolated and have separate cost attribution and quotas. Explain how to enforce tenant isolation, cost allocation, and prevent noisy-neighbor problems. Include IAM, naming/partitioning, tagging, and compute isolation strategies.

HardTechnical

55 practiced

An analytics team wants to run machine learning feature engineering in-place against a large cloud data lake. Evaluate trade-offs between: 1) moving data into a dedicated feature store, 2) running ad-hoc Spark jobs against object storage, and 3) using a serverless SQL engine. Consider latency, reusability, consistency, and cost.

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