Distributed Computing Mid

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Explain the concept of data locality in distributed computing.

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1

Data locality in distributed computing refers to processing data close to where it is stored to reduce latency.

2

Data locality means storing all data on a single server.

3

Data locality refers to encrypting data for security.

4

Data locality is about backing up data in a local storage device.

What are the challenges of handling network latency in distributed systems, and what techniques can be used to mitigate latency-related issues?

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1

Handling network latency in distributed systems involves techniques like caching and data replication.

2

Network latency does not affect distributed systems.

3

Handling network latency is solely about increasing the bandwidth.

4

Network latency can be ignored in distributed systems.

What are the challenges associated with managing distributed system logs, and how can these challenges be addressed?

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1

Distributed system logs do not require real-time analysis.

2

Managing distributed system logs is simple due to their uniform structure.

3

Managing distributed system logs is challenging due to their volume, variety, and the need for real-time analysis.

4

Managing distributed system logs only involves backing up data.

Explain the concept of distributed tracing and its importance in monitoring and debugging microservices architectures.

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1

Distributed tracing tracks requests as they flow through various services in a microservices architecture.

2

Distributed tracing is used to encrypt data across microservices.

3

Distributed tracing is a method for data backup in microservices.

4

Distributed tracing speeds up processing in microservices architectures.

Explain the concept of the Two-Phase Commit (2PC) protocol and its use in distributed transaction management.

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1

The Two-Phase Commit (2PC) protocol is used to encrypt data in a distributed system.

2

The Two-Phase Commit (2PC) protocol is a method for data backup.

3

The Two-Phase Commit (2PC) protocol ensures all nodes in a distributed system either commit or abort a transaction.

4

The Two-Phase Commit (2PC) protocol speeds up data processing in a single-node system.

What is the role of a load balancer in a distributed system, and how does it work?

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1

A load balancer is used to encrypt data before transmission.

2

A load balancer distributes network or application traffic across multiple servers to ensure no single server becomes overwhelmed.

3

A load balancer only works in single-node systems.

4

A load balancer stores backup data for recovery purposes.

Explain the concept of leader election in distributed systems and its role in fault tolerance.

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1

Leader election is about backing up data in a distributed system.

2

Leader election in distributed systems determines which node will act as the coordinator for a task.

3

Leader election is a process of encrypting data in distributed systems.

4

Leader election ensures all nodes have the same processing speed.

Explain the concept of distributed consensus and the role of algorithms like Paxos and Raft.

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1

Distributed consensus ensures data encryption across all nodes.

2

Distributed consensus is achieved when all nodes in a distributed system agree on a common state.

3

Distributed consensus is about achieving the highest processing speed in a distributed system.

4

Distributed consensus is a method for data replication.

What is a distributed lock, and why is it important in distributed systems?

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1

A distributed lock ensures that only one process can access a resource at a time, preventing conflicts.

2

A distributed lock speeds up data processing by allowing multiple processes to access the same resource.

3

A distributed lock is a backup mechanism for data recovery.

4

A distributed lock is used to encrypt data across multiple nodes.

Explain the concept of eventual consistency in distributed databases.

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1

Eventual consistency means that updates are immediately visible to all nodes.

2

Eventual consistency means that updates to a distributed database will eventually be reflected across all nodes.

3

Eventual consistency guarantees real-time data replication.

4

Eventual consistency ensures data is never lost in a distributed system.

Explain the concept of distributed caching and its benefits in distributed systems.

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1

Distributed caching is used to synchronize data between different databases.

2

Distributed caching stores frequently accessed data across multiple nodes to improve performance.

3

Distributed caching is a method for data encryption.

4

Distributed caching is a security measure to prevent data breaches.

What is MapReduce, and how does it work in the context of distributed computing?

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1

MapReduce is a programming model for processing large data sets with a distributed algorithm on a cluster.

2

MapReduce is a type of database management system.

3

MapReduce is a networking protocol for distributed systems.

4

MapReduce is a file compression algorithm used in distributed systems.