Distributed Systems and Consensus Algorithms Questions

Covers the fundamentals and practical application of distributed systems and the algorithms that produce agreement across nodes. Topics include consistency models such as strong consistency, eventual consistency, and causal consistency; replication strategies including master slave and peer to peer; and distributed transactions. Explain consensus algorithms and their trade offs, for example Raft, Paxos, and Practical Byzantine Fault Tolerance, including safety and liveness properties, quorum based decision making, leader election patterns, leader based versus leaderless designs, log replication, heartbeats, and lease based leadership. Understand the theorem that trades off consistency, availability, and partition tolerance and its implications for system design. Be able to reason about failure modes such as network partitions, split brain, and Byzantine faults and how algorithms and system choices mitigate them. Include familiarity with real world systems that implement these concepts such as Etcd, Consul, ZooKeeper, DynamoDB, and HBase. At senior and staff levels, demonstrate when consensus is required versus when it can be avoided, the operational complexity and performance costs of consensus, practical engineering decisions for scaling and fault tolerance, debugging and testing strategies, and how these choices affect application behavior.