Quiz

2667 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 areas2667 Architecture Fundamentals81 Distributed Systems101 Data Architecture90 Cloud Architecture87 Networking86 API & Integration Architecture78 Reliability & Resilience88 Observability81 Performance & Capacity Engineering90 Security Architecture95 Cost Architecture & FinOps92 Business Architecture93 Architecture Communication91 Enterprise Architecture91 Legacy Modernization92 AI-Era Architecture86 Software Architecture & Engineering84 Architecture Patterns84 Architecture Decision-Making91 The Architect's Meta-Skills92 Delivery & Release Engineering93 Platform Engineering & Developer Experience92 Testing & Quality Architecture90 Data Platform Architecture88 Streaming & Real-Time Data93 Data Governance & Semantics81 Frontend & Experience Architecture91 Edge, Mobile & IoT88 Regulatory & Data Protection Architecture90 Assurance, Audit & Model Risk88

Difficulty Any beginner intermediate advanced

1544 questions.

Quiz

An AI gateway now terminates streaming responses for six products and holds a semantic cache shared across tenants. Two properties of that design have caused real outages and real data exposure at large AI providers. What has the organisation taken on and when does the bill arrive?

A fraud model has been in production for eight months. Ground truth arrives weeks later. How do you know if it is still working?

What must be observable in an AI feature that is not covered by conventional application monitoring?

You are asked to log every prompt and response for an assistant handling 2 million interactions a day, so quality regressions are diagnosable. Roughly how much data does that commit you to per year, and does the number change the design?

An enterprise AI provider must apply governance proportionate to risk across many model deployments. How should tiering work?

An organisation deploys many AI systems with very different risk profiles. How should they be tiered, and which controls attach to each tier?

How should AI systems be risk-tiered so that governance is proportionate?

The business wants to deploy a model that ranks loan applications, with a credit officer making the final decision. What must the architecture provide, and what will you insist on before go-live?

Your company is deploying twelve AI features. Compliance wants one governance process for all of them. What do you propose?

Your team receives 200 pages a week and the on-call rotation has lost two engineers in six months. Fix it.

Error rate has been 1.5% for three days. No alert fired. Customers are complaining. What is happening and what failed?