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Distributed Systems Practice Questions: Test Your Knowledge | LearnByTeaching.ai

Challenge your understanding of distributed systems with these 40 questions covering consensus, replication, consistency models, and fault tolerance. These questions test concepts from Lamport's foundational work through modern cloud-native architectures, at a level appropriate for graduate students and senior engineers.

40 questions total

Consensus and Leader Election

Test your understanding of consensus and leader election.

Q1Easyconsensus-algorithms

In the Raft consensus algorithm, what is the minimum number of nodes that must agree for a log entry to be committed in a 5-node cluster?

Q2Mediumconsensus-algorithms

What problem does the Paxos 'prepare' phase solve that a naive 'just propose a value' approach would not?

Q3Mediumconsensus-algorithms

In a Raft cluster, a leader becomes network-partitioned from the majority but can still communicate with two followers. What happens?

Q4Hardconsensus-algorithms

Which of the following is NOT a property guaranteed by the FLP impossibility result?

Q5Mediumconsensus-algorithms

What is the primary advantage of Multi-Paxos over basic Paxos?

Q6Easyleader-election

In leader election, what is the purpose of randomized election timeouts in Raft?

Q7Easyconsensus-algorithms

What is the Byzantine Generals Problem fundamentally about?

Q8Mediumconsensus-algorithms

A ZooKeeper ensemble of 7 nodes experiences the failure of 3 nodes. Can the system still process write requests?

Q9Easyconsensus-algorithms

What does the 'term' number in Raft represent?

Q10Hardconsensus-algorithms

In PBFT (Practical Byzantine Fault Tolerance), why are three phases (pre-prepare, prepare, commit) needed instead of two?

Replication and Consistency Models

Test your understanding of replication and consistency models.

Q11Easyconsistency-models

Which consistency model guarantees that if process A completes a write before process B starts a read, B will see A's write?

Q12Hardconsistency-models

What is the key difference between linearizability and serializability?

Q13Hardreplication

In a system using quorum-based replication with N=5 replicas, W=3 write quorum, and R=3 read quorum, is strong consistency guaranteed?

Q14Mediumclock-synchronization

What is the main purpose of vector clocks in distributed systems?

Q15Mediumreplication

In Amazon's Dynamo, what technique is used to resolve write conflicts when concurrent updates occur?

Q16Easyconsistency-models

What does 'read-your-writes' consistency guarantee?

Q17Easyconsistency-models

Why might a system choose eventual consistency over strong consistency?

Q18Mediumreplication

In chain replication, how do reads and writes flow?

Q19Mediumconsistency-models

What is a CRDTs (Conflict-free Replicated Data Type) main advantage in distributed systems?

Q20Easyreplication

What is the difference between primary-backup replication and multi-leader replication?

Fault Tolerance and Partitioning

Test your understanding of fault tolerance and partitioning.

Q21Easyfault-tolerance

According to the CAP theorem, which property must be sacrificed during a network partition?

Q22Mediumfault-tolerance

What is a split-brain scenario in distributed systems?

Q23Easypartitioning

What is consistent hashing primarily used for in distributed systems?

Q24Hardclock-synchronization

In Google's Spanner, how does TrueTime enable externally consistent transactions?

Q25Mediumfault-tolerance

What is a fencing token and why is it used?

Q26Mediumconsistency-models

What does the PACELC theorem add to the CAP theorem?

Q27Mediumfault-tolerance

In a distributed system, what is a 'gray failure'?

Q28Mediumreplication

What is the purpose of anti-entropy protocols like Merkle trees in distributed storage?

Q29Easyfault-tolerance

How does the sidecar pattern improve fault tolerance in microservice architectures?

Q30Easyclock-synchronization

What is the fundamental problem with using wall-clock timestamps for ordering events in a distributed system?

Distributed Transactions and Data Processing

Test your understanding of distributed transactions and data processing.

Q31Mediumdistributed-transactions

In two-phase commit (2PC), what happens if the coordinator crashes after sending 'prepare' but before sending 'commit' or 'abort'?

Q32Mediumdistributed-transactions

What is the Saga pattern in distributed systems?

Q33Easymapreduce-and-batch-processing

In the MapReduce programming model, what is the role of the shuffle phase between Map and Reduce?

Q34Easystream-processing

What advantage does event sourcing provide over traditional CRUD in distributed systems?

Q35Harddistributed-transactions

How does three-phase commit (3PC) improve upon two-phase commit (2PC)?

Q36Mediumstream-processing

In stream processing, what is the difference between event time and processing time?

Q37Hardstream-processing

What is exactly-once semantics in stream processing, and how is it typically achieved?

Q38Mediumdistributed-transactions

What problem does the outbox pattern solve in microservice architectures?

Q39Mediummapreduce-and-batch-processing

In Google's MapReduce, what is the purpose of the 'backup task' mechanism?

Q40Hardfault-tolerance

What is a distributed snapshot, and which algorithm is most commonly associated with it?

Scoring Guide

Total possible: 40

Excellent36-40: Outstanding command of distributed systems theory and practice. Ready for systems design interviews at top companies.
Good28-35: Solid understanding of core concepts. Review consistency models and fault tolerance edge cases for deeper mastery.
Needs WorkBelow 28: Revisit foundational material — start with Designing Data-Intensive Applications by Kleppmann and the Raft paper.

Study Recommendations

  • Read Designing Data-Intensive Applications by Martin Kleppmann cover to cover — it is the single best resource for building distributed systems intuition
  • Study the Raft consensus paper in detail and implement a basic version to solidify your understanding
  • Review Jepsen test reports to understand how real distributed databases fail under network partitions
  • Practice system design interview problems that require reasoning about consistency, availability, and partition tolerance tradeoffs
  • Work through MIT 6.824 Distributed Systems labs for hands-on experience with real distributed protocols
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