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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
45 practiced
You manage a Kubernetes-based training cluster that often uses more nodes than necessary during nightly jobs. Describe three engineering strategies to reduce overprovisioning and waste in this cluster (consider scheduling, autoscaler configuration, and workload packaging).
MediumTechnical
79 practiced
When using a managed feature store (online + offline), how would you evaluate and optimize its cost? Discuss trade-offs between storing dense features in the online store versus computing them on the fly, and propose monitoring signals to identify cost hotspots.
MediumTechnical
54 practiced
Propose a cost-aware CI/CD strategy for ML model development. Requirements: prevent accidental expensive runs in CI, enable cheap smoke tests for model changes, and allow gated full-scale training that requires approval. Describe pipeline stages, resource quotas, and gating mechanisms.
MediumTechnical
53 practiced
Propose a set of KPIs and dashboard views that an ML team should present to product and finance stakeholders to justify investment in cost optimization work. Include at least three KPIs and explain the target audience and recommended visualization for each.
HardSystem Design
58 practiced
Design a training platform that leverages spot instances across multiple availability zones and instance pools for resilience. Provide details on checkpointing cadence, how to schedule retries, policies to select instance pools, and a cost-vs-completion-time model that guides when to fall back to on-demand instances.

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