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CAP Theorem and Consistency Models Questions

Understand the CAP theorem and how Consistency, Availability, and Partition Tolerance interact in distributed systems. Know different consistency models including strong consistency such as linearizability, eventual consistency, causal consistency, and session consistency, and how to apply them to different use cases. Be familiar with consensus protocols and distributed coordination primitives such as Raft and Paxos, quorum reads and writes, two phase commit and when to use them. Understand trade offs between consistency and availability under network partitions, patterns for hybrid approaches where different data uses different guarantees, and the product and developer experience implications such as latency, stale reads, and API contract clarity.

HardSystem Design
0 practiced
Multiple services must update feature transformations and model metadata atomically across microservices when deploying a new model. Propose using the saga pattern versus distributed transactions (2PC). Describe the coordinator's responsibilities, compensation actions, ordering guarantees, and failure modes you must handle for safe rollouts.
HardSystem Design
0 practiced
Architect a global feature store that supports strong consistency for a subset of critical features and eventual consistency for others. Describe the components, replication protocols, metadata/versioning approach, client routing logic, and conflict-resolution strategies to support mixed guarantees while maintaining low latency.
HardTechnical
0 practiced
Design a streaming ingestion pipeline for feature computations with exactly-once semantics for downstream feature stores used in training. Discuss checkpointing, idempotent writes, transactional sinks, integration options with systems like Kafka and Flink, and recovery behavior after failures to preserve exactly-once guarantees.
MediumTechnical
0 practiced
When using vector clocks or timestamps for conflict detection in feature updates across regions, how would you implement conflict resolution for numeric aggregations used as model inputs, such as click counts or dwell-time averages? Discuss merging strategies and statistical effects on downstream models.
EasyTechnical
0 practiced
Define linearizability and serializability and contrast them in plain terms. For an online prediction API used for sequential operations (for example, financial risk scoring where order matters), which model is most applicable and why? Describe the operational consequences of choosing the wrong model.

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