Interview prompt
Problem context
Skills being evaluated
Use the sequence below to surface constraints, choose boundaries, test failure behavior, and defend trade-offs. Concrete numbers are interview assumptions, not claims about a real production system.
Clarify the decision
- Identify recurring costly decisions and enterprise risks that justify consistency. Distinguish principles, mandatory policies, reference architectures, and optional guidance.
Establish scale assumptions
- Measure incident patterns, duplicated implementations, decision delay, exceptions, and platform adoption across thirty teams. Standardize where shared learning and risk exceed local differentiation.
Functional and non-functional requirements
- Principles are few, contextual, testable, and supported by tools; teams retain local decisions inside guardrails. Exceptions are fast, visible, and feed principle evolution.
High-level architecture
- Publish each principle with rationale, decision rule, examples, counterexamples, owner, automated checks, paved-road support, and exception process. A federated council reviews patterns and systemic exceptions.
Data model and flow
- Architecture decisions and exceptions enter a searchable record; platform telemetry and incidents reveal where guidance succeeds or creates friction. Changes cite the evidence.
Consistency and transaction boundaries
- Mandatory controls have clear enforcement and risk authority; principles guide trade-offs rather than pretending one design fits all. Versions and effective dates prevent silent governance changes.
Failure modes and recovery
- Avoid central preapproval for routine compliant choices, stale documents, and permanent exceptions. Emergency deviations expire and receive post-incident review.
Security and privacy
- Security policies define required outcomes and provide paved-road implementations. Teams cannot waive statutory controls through ordinary architecture exception.
Observability and SLOs
- Track decision lead time, automated compliance, exception age and clusters, platform adoption, repeated incidents, and principle usefulness surveys. Fewer exceptions is not always better.
Capacity and cost
- Fund tooling for high-value principles; unfunded standards merely transfer toil to product teams. Retire guidance whose coordination cost exceeds its risk reduction.
Alternatives and trade-offs
- Detailed standards improve consistency but age quickly; abstract principles are durable but unactionable. Pair durable intent with maintained reference implementations and tests.
Evolution and migration
- Pilot principles in active decisions, automate the most repeated checks, review exceptions quarterly, and revise with versioned evidence. Keep the set intentionally small.
What Staff and Principal candidates should emphasize
- Staff candidates design governance as a product and incentive system. The easiest path should be safe, while expert deviations remain possible and accountable.
Decision trade-offs
Governance
Option A
Central architecture approval board
Option B
Federated ownership with automated guardrails
Recommendation:Automate routine risk controls and federate domain decisions; reserve central review for cross-cutting irreversible choices and exceptions.
Guidance form
Option A
Detailed mandated reference architecture
Option B
Principle plus supported paved roads
Recommendation:State durable decision intent and offer maintained implementations, allowing evidence-backed alternatives that meet the same outcome.
Follow-up interview questions
- 01What makes a principle testable?
- 02Who can approve an exception?
- 03How do repeated exceptions change the standard?
- 04Which decisions should remain local?
Common weak answers and mistakes
- 01Publishing slogans such as scalable and secure with no decision rule.
- 02Creating a review board for every service change.
- 03Mandating standards without funded tooling or migration support.
- 04Judging success only by declining exception count.
Interviewer evaluation rubric
Writes broad standards or forms a committee without enforcement, support, exceptions, or outcome evidence.
Defines few contextual principles, paved roads, automated checks, federated owners, and fast exceptions.
Versions guidance, distinguishes policy, measures decision outcomes, funds tooling, and learns from exception clusters.
Creates an adaptive architecture institution where autonomy and enterprise learning reinforce rather than oppose each other.