Translating Business Problems to Computational Solutions Questions
Techniques and practices for analyzing business problems, defining success criteria, and translating them into concrete computational requirements, features, user stories, and architectural decisions. Aligns business goals with product strategy, roadmap, and delivery plans; emphasizes stakeholder collaboration, scoping, prioritization, and value-driven thinking.
EasySystem Design
0 practiced
A business-level SLA states: 'API should respond within 200ms for 95% of requests.' Describe how you would represent that as SLIs, set up monitoring requirements (metrics and alert thresholds), and specify what architectural choices support achieving it (caching, CDNs, autoscaling).
HardTechnical
0 practiced
Create a technical plan to quantify and reduce 'cost of delay' for a product roadmap of 8 candidate features over 12 months. Include required data inputs, a Monte Carlo approach (or simpler model) to estimate expected lost value, a prioritization algorithm, and how results are communicated to executives.
HardTechnical
0 practiced
For a ride-sharing analytics platform with 100M monthly users, propose data partitioning and query strategies so near-real-time dashboards (sub-minute latency) and ad-hoc analytics coexist without impacting transactional databases. Include data lake vs OLAP choices and example ETL cadence.
MediumTechnical
0 practiced
As a Solutions Architect, create a comparative TCO analysis outline for 'build custom search' vs 'use managed SaaS search'. List the cost categories, risk factors, and show an example table of one-year vs three-year costs and qualitative trade-offs you'd present to the customer.
HardTechnical
0 practiced
Design a low-latency (<100ms) fraud detection architecture for high-value transactions that satisfies explainability for regulators. Include streaming scoring, feature store design, model serving strategy, fallback synchronous checks, and how to capture audit trails for decisions.
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