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2908 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 areas2908 Architecture Fundamentals91 Distributed Systems101 Data Architecture90 Cloud Architecture87 Networking86 API & Integration Architecture87 Reliability & Resilience99 Observability92 Performance & Capacity Engineering100 Security Architecture95 Cost Architecture & FinOps102 Business Architecture103 Architecture Communication102 Enterprise Architecture100 Legacy Modernization92 AI-Era Architecture96 Software Architecture & Engineering93 Architecture Patterns94 Architecture Decision-Making101 The Architect's Meta-Skills102 Delivery & Release Engineering103 Platform Engineering & Developer Experience102 Testing & Quality Architecture102 Data Platform Architecture98 Streaming & Real-Time Data103 Data Governance & Semantics91 Frontend & Experience Architecture101 Edge, Mobile & IoT98 Regulatory & Data Protection Architecture99 Assurance, Audit & Model Risk98
Difficulty Any beginner intermediate advanced

103 questions in Streaming & Real-Time Data.

  1. CDC to Stream advanced

    A team proposes exposing their service's database change stream via CDC so other teams can consume it, avoiding the work of building an event API. What is your assessment?

    2 min answer cdcintegrationcouplingcontracts
  2. Dead Letter Handling beginner Multiple choice

    A platform team sets one default for every stream consumer: retry a failing record three times then route it to a dead-letter topic and carry on. The ledger team's consumer derives account balances from ordered per-account events. What trade-off does that default make on their behalf and what should they run instead?

    3 min answer dead letterorderingavailabilitycorrectness
  3. Dead Letter Handling intermediate

    A streaming pipeline sends failed messages to a dead-letter queue. What makes that a real control rather than a data-loss mechanism?

    2 min answer segmentdead-letterpoisonownership
  4. Dead Letter Handling intermediate

    How should unprocessable messages be handled, and what makes dead-letter handling useful rather than a graveyard?

    2 min answer dead-letterpoison-messagesreplayvisibility
  5. Dead Letter Handling intermediate

    You find 40,000 messages in a dead letter queue, the oldest from seven months ago. Nobody knew. What do you fix?

    2 min answer messagingoperationsdata-loss
  6. Exactly-Once Semantics beginner Multiple choice

    A consumer reads a record, writes a row to a database, then commits its offset. An engineer swaps the last two lines so the offset is committed first, reasoning that this way a record can never be processed twice. The consumer is killed roughly twice a week by deploys and node rotations. Which ordering should be the default?

    3 min answer kafkaoffsetsat-least-onceidempotency
  7. Exactly-Once Semantics advanced

    A payments team requires exactly-once processing of a transaction stream. What is actually achievable, and what should be built?

    2 min answer phonepeexactly-onceidempotencytransactions
  8. Exactly-Once Semantics advanced

    A team requires exactly-once processing across a message platform and its external providers. What is achievable, and what should be built instead?

    2 min answer exactly-onceidempotencyat-least-onceboundaries
  9. Exactly-Once Semantics advanced

    A team wants exactly-once processing in a streaming pipeline. Where do transactional producers, idempotent consumers and output-side deduplication each fit, and which failure modes still require reconciliation?

    3 min answer kafkaexactly-onceidempotencytransactions
  10. Exactly-Once Semantics advanced

    A vendor claims their streaming platform provides exactly-once processing. How do you evaluate the claim?

    1 min answer streamingsemanticsvendorcorrectness
  11. Exactly-Once Semantics advanced

    Your streaming job has exactly-once semantics enabled. Customers report receiving the same email twice. Explain.

    2 min answer streamingdelivery-guaranteesidempotency
  12. Feature Freshness advanced

    ByteDance's Monolith paper (ORSUM at RecSys 2022) describes a collisionless embedding table for a recommender trained on the interaction stream, where new user and item ids arrive continuously so the key space has no ceiling. What bounds the memory, and what does each bound cost in model quality?

    3 min answer bytedancemonolithembeddingsonline training