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Major Technical Decisions and Trade Offs Questions

Behavioral and leadership oriented topic asking candidates to present real examples of significant technical decisions they influenced. Candidates should prepare two to three concrete examples that cover the problem context, options considered, reasoning and evaluation of trade offs, stakeholder engagement and buy in, the final decision, implementation approach, measured outcomes, and retrospective lessons including what they would do differently. This topic assesses ownership, influence, communication, cross functional collaboration, and ability to defend and learn from organizational level technical choices.

EasyTechnical
0 practiced
Tell me about a time you decided to adopt a managed cloud service instead of self-hosting a component (or vice versa). Walk through evaluation criteria you used (SLA, operational overhead, security/compliance, integration, vendor lock-in risk, TCO), why you chose one path, and how you mitigated long-term risks like lock-in.
HardTechnical
0 practiced
You need to design a data platform with privacy-by-design for sensitive PII data vs retrofitting privacy controls later. Explain trade-offs in cost, development time, business agility, and compliance risk. Recommend an approach for a growth-stage company and a plan to implement it.
MediumSystem Design
0 practiced
You propose implementing data contracts between producers and consumers. Describe the contract structure (schema, semantic expectations, SLAs), how to enforce contracts in CI and at runtime, a migration plan for non-compliant producers, and how you would measure adoption and effectiveness.
MediumTechnical
0 practiced
A legacy daily ETL job is taking many hours to complete and blocking analytics. Explain how you'd decide whether to rewrite it in Spark, optimize the SQL, or change to incremental loads. Include evaluation criteria, estimated ROI, risk assessment, and rollout approach.
MediumSystem Design
0 practiced
Design an SLO/SLA framework for a family of data pipelines that feed analytics and ML features. Specify key metrics (freshness, completeness, error-rate), alert thresholds, escalation paths, and how you would operationalize runbooks and consumer communication.

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