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Cloud Cost Optimization and Financial Operations Questions

Covers strategies and organizational practices for minimizing and managing cloud and infrastructure spend while balancing performance, reliability, and business priorities. Candidates should understand cloud cost drivers such as compute, storage, data transfer, and managed services; pricing models including on demand pricing, reserved capacity commitments, savings plans, and interruptible or spot offerings; and engineering techniques that reduce spend such as rightsizing, autoscaling, storage tiering, caching, and workload placement. This topic also includes financial operations practices for continuous cost management and governance: resource tagging and cost allocation, budgeting and forecasting, chargeback and showback models, anomaly detection and alerting, cost reporting and dashboards, and processes to gate changes that affect spend. Interviewees should be able to estimate recurring costs and total cost of ownership, identify and quantify optimization opportunities, weigh trade offs between cost and business objectives, and describe tools and metrics used to monitor and communicate cost to stakeholders.

HardTechnical
0 practiced
For petabyte-scale Spark ETL, detail advanced optimizations that reduce cost: partitioning strategy, shuffle tuning, broadcast vs shuffle join thresholds, file format & compaction, speculative/executor management, instance-family selection, and spot instance handling. For each optimization, explain how it affects job runtime, memory usage, and cost and any measurable trade-offs.
HardSystem Design
0 practiced
Design Kubernetes cost optimization for scalable data processing jobs on a cluster with mixed workloads (batch ETL, streaming, interactive notebooks). Include node pool strategies, Spot/preemptible usage, pod and cluster autoscaling settings, namespace quotas, admission controls, metrics to monitor, and how to attribute cost back to teams.
EasyTechnical
0 practiced
A set of ETL clusters has an average CPU utilization of 20% across the day and your monthly compute bill is unusually high. As a data engineer, outline immediate and short-term rightsizing steps you would take to reduce cost with minimal risk to pipeline SLAs. Include quick wins and safety checks.
EasyTechnical
0 practiced
Your company runs multiple teams that provision cloud resources ad hoc. Describe a practical resource tagging and cost-allocation strategy that a data engineering org should adopt to enable accurate monthly chargeback or showback. Include tag naming conventions, enforcement options, and how to handle untagged resources.
EasyTechnical
0 practiced
Explain common cloud pricing models relevant to data platforms: on-demand, reserved/committed capacity, savings plans, and interruptible/spot/preemptible instances. For each model describe the typical discount range, risk profile, and the types of data workloads (batch ETL, interactive queries, training jobs) where you would and would not recommend using it.

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