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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.

MediumTechnical
48 practiced
Describe three practical architecture patterns and operational changes to reduce cross-region data transfer for a global data pipeline that collects telemetry from multiple regions. Explain pros/cons for each (e.g., local aggregation + central analytic store, per-region compute with federated queries, compressed batched transfers).
MediumTechnical
53 practiced
Given a billing_events table (columns: event_date DATE, service STRING, amount_usd FLOAT), write a SQL query to flag any service that has spending for the current week greater than 150% of its 3-week moving average. Use standard SQL and be explicit about window functions or aggregates.
HardTechnical
49 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
55 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
50 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.

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