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.

MediumTechnical

51 practiced

Design a monitoring and alerting plan for a cloud data platform that includes: ingestion pipelines, streaming lag, data freshness SLAs, query errors, and cross-region replication health. Specify key metrics, alert thresholds, and who to notify in each failure scenario.

MediumTechnical

61 practiced

Design an approach to perform cost forecasting and daily budget enforcement for cloud data processing jobs (ETL/streaming/queries). Describe how you would implement tagging, cost allocation, alerts, automated throttling, and owner accountability for unexpected cost spikes.

MediumTechnical

51 practiced

You observe read replica lag in a managed relational store that feeds analytics. Outline debugging steps to find the root cause (IOPS starvation, network, DDL, hot transactions), and remediation options to reduce replication lag and maintain analytics freshness.

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.

HardTechnical

53 practiced

A customer's data pipeline relies on a managed service that imposes a 2,000 partition limit per table which is causing ETL failures as data grows. Devise strategies to work around the partition count limit for a cloud data warehouse and explain trade-offs for each approach (e.g., hierarchical partitioning, bucketing, time-bucketing, prefixing).

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