Quiz

3114 questions of the kind that actually get asked — in interviews, in architecture review boards, and by the person who has to run the thing at 3 AM. Every answer states the trade-off rather than the slogan, and says when the obvious choice is the wrong one.

All areas3114 Architecture Fundamentals91 Distributed Systems111 Data Architecture100 Cloud Architecture97 Networking96 API & Integration Architecture97 Reliability & Resilience99 Observability102 Performance & Capacity Engineering100 Security Architecture105 Cost Architecture & FinOps102 Business Architecture103 Architecture Communication102 Enterprise Architecture111 Legacy Modernization102 AI-Era Architecture106 Software Architecture & Engineering103 Architecture Patterns94 Architecture Decision-Making112 The Architect's Meta-Skills113 Delivery & Release Engineering103 Platform Engineering & Developer Experience102 Testing & Quality Architecture113 Data Platform Architecture108 Streaming & Real-Time Data103 Data Governance & Semantics101 Frontend & Experience Architecture112 Edge, Mobile & IoT109 Regulatory & Data Protection Architecture109 Assurance, Audit & Model Risk108

Difficulty Any beginner intermediate advanced

3114 questions.

Quiz

A collaborative product with long-lived client connections needs zero-downtime releases. How do rolling, blue-green and canary differ here?

A regulated business releases every two weeks through a change advisory board. Engineering wants continuous deployment. The control is real: an auditor tests that every production change was authorised, risk-assessed, tested and reversible. Sequence the migration.

At 14:05 a canary on 5% of traffic passes automated analysis across 60 metrics and is promoted. At 14:40 the fleet error rate is 12 times baseline. The canary's own dashboards were clean the entire time. What made the canary blind, and which design decision allowed it?

You roll back a deployment and the system is still broken. What are the likely reasons?

A grocery delivery platform experiences a sudden multi-week increase in demand well beyond any forecast. Which capacity constraints bind first, and which cannot be solved with autoscaling?

A service runs across three availability zones at 70% CPU during peak. The team wants to survive losing one zone at peak with no user impact. Roughly what does that require, and what does the same arithmetic say about running across two zones?

How should a platform with extreme scheduled peaks decide how much capacity headroom to hold, and what evidence should drive it?

Your platform runs across three availability zones. How would you determine whether it actually survives losing one?

An airline's crew-scheduling system cannot re-plan fast enough during cascading weather disruption, forcing manual processes that cannot keep up. What signals indicated this risk earlier, and how should such a system be modernised?

An organisation adopts blameless postmortems but engineers still avoid admitting mistakes and incidents are under-reported. What is missing beyond the stated policy?