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

59 questions in AI-Era Architecture.

Quiz

A model performs well in evaluation and poorly in production. What are the likely causes?

A recommendation platform's ML systems span data pipelines, training, evaluation and online serving. Where should the boundaries be, and what causes the most costly class of bug?

A training run on thousands of GPUs loses several nodes mid-run. How should checkpointing frequency, elastic training, straggler detection and scheduling minimise wasted compute, and what does each checkpoint cost?

A platform must choose which model serves which request class. What should drive the decision, and what changes over time?

A research assistant runs a planner that fans out to four worker agents and a synthesiser that writes the final answer. One worker retrieves the wrong document and returns a fluent confident summary of it. What happens downstream and what stops it?

A team proposes decomposing a workflow into several specialised agents that coordinate. What justifies this over a single agent with more tools, and what does it cost?

A team proposes five specialised agents that collaborate to handle a customer request. Assess.

Interview prompt. A supervisor agent fans out to five worker agents that each call tools with real side effects - creating tickets, sending emails, updating records. One worker fails at its fourth tool call, after two of those side effects have already happened. Tell me what the system does next.

An AI assistant reads code and issues from repositories, including untrusted ones, and can call tools. Why is prompt injection a structural problem, and what actually mitigates it?

An agent reads customer emails and can issue refunds. What is the threat and how do you bound it?

An agent with tool access processes untrusted content. Why is prompt injection not solvable at the prompt layer, and what architectural controls actually limit the damage?

An application passes user input into a model that can call tools. What is the threat, and where must the control live?