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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.

  1. Prompt & Version Management advanced

    140 prompts live as string literals across nine services. You must move them into a central prompt registry with no quality regression and no big-bang cutover, while every team keeps shipping. What is the sequence, and where can it go wrong?

    3 min answer prompt-managementmigrationversioningevaluation
  2. RAG Architecture advanced

    A RAG system returns fluent answers that are frequently wrong. Where in the pipeline is the problem most likely, and how would you find out?

    2 min answer meeshoragretrievalevaluation
  3. RAG Architecture advanced

    A retrieval-augmented system over enterprise documents gives confidently wrong answers. Which failure modes are responsible, and where should engineering effort go?

    2 min answer ragretrievalgroundingpermissions
  4. RAG Architecture advanced

    A retrieval-augmented system serves millions of documents with sub-100 ms latency while embeddings are regenerated nightly. How should index rebuilds, hybrid retrieval, filtering and embedding versions be handled?

    3 min answer ragvector-searchindex-rebuildhybrid-retrieval
  5. RAG Architecture advanced

    A user asks the internal assistant a question and receives content from a document they cannot access. How did this happen and how is it prevented?

    2 min answer ragauthorizationsecurity
  6. RAG Architecture advanced

    An internal AI assistant gives confidently wrong answers. The team wants to upgrade to a better model. What do you check first?

    2 min answer airagretrievalevaluation
  7. Reranking advanced

    A support assistant adds a cross-encoder reranker. Offline NDCG at 10 rises from 0.61 to 0.74 and it ships. Two weeks later, answers drawn from long runbook pages are worse than they were before reranking, while short FAQ answers improved. Nothing errors and no alert fires. What happened?

    3 min answer rerankingcross-encodertruncationevaluation
  8. Tool Calling advanced

    A commerce platform exposes tools for a model to call. How should the tool interface be designed, and what differs from designing an API for developers?

    2 min answer tool-callinginterface-designerrorssafety
  9. Tool Calling advanced

    An agent can query the customer database, send emails and issue refunds. What is your security design?

    2 min answer agentssecurityauthorization
  10. Vector Databases advanced Multiple choice

    A multi-tenant assistant searches one HNSW index of 200M vectors and filters results to the requesting tenant. Large tenants are fine. For tenants with only a few thousand documents recall collapses and latency triples. Which change fixes it?

    3 min answer vector-databasehnswmulti-tenancyfiltering
  11. Vector Databases advanced Multiple choice

    A platform needs similarity search over a large and growing corpus. What decides between a dedicated vector database, a vector extension to an existing store, and a search engine with vector support?

    2 min answer vector-databaseoperational-costfilteringscale