Distributed Computing Expert

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Explain the concept of vector clocks in distributed systems and how they help in determining causality among events.

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1

Vector clocks in distributed systems help determine the order of events and causality among them.

2

Vector clocks are used to encrypt data in distributed systems.

3

Vector clocks ensure real-time data processing.

4

Vector clocks are used for data backup in distributed systems.

Explain the concept of distributed pub-sub (publish-subscribe) messaging systems and their use in event-driven architectures.

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1

Distributed pub-sub systems ensure data is backed up in real-time.

2

Distributed pub-sub systems are used for real-time data encryption.

3

Distributed pub-sub systems allow for asynchronous communication between publishers and subscribers in event-driven architectures.

4

Distributed pub-sub systems store data on a single server.

What is the role of ZooKeeper in distributed systems?

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1

ZooKeeper is a load balancing tool for distributed systems.

2

ZooKeeper is only used for storing distributed databases.

3

ZooKeeper is used for coordinating distributed applications through services like configuration management and synchronization.

4

ZooKeeper is a data encryption tool for distributed systems.

What is the purpose of a distributed task queue in distributed systems?

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1

A distributed task queue encrypts data before processing.

2

A distributed task queue manages and distributes tasks among multiple servers for efficient processing.

3

A distributed task queue ensures data is backed up during processing.

4

A distributed task queue is used only for data storage.

What is the purpose of a distributed lock manager (DLM) in distributed systems?

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1

A distributed lock manager (DLM) ensures that distributed systems can safely manage shared resources.

2

A distributed lock manager (DLM) is used to encrypt data across nodes.

3

A distributed lock manager (DLM) stores backup data for recovery.

4

A distributed lock manager (DLM) ensures that all data is processed in real-time.

Explain the concept of distributed scheduling.

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1

Distributed scheduling coordinates the execution of tasks across multiple computing nodes.

2

Distributed scheduling is only used in single-node systems.

3

Distributed scheduling ensures data is backed up before processing.

4

Distributed scheduling involves encrypting data before execution.

Explain the concept of data locality in distributed computing.

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1

Data locality means storing all data on a single server.

2

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

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

Network latency does not affect distributed systems.

2

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

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 only involves backing up data.

3

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

4

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

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

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1

Distributed tracing speeds up processing in microservices architectures.

2

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

3

Distributed tracing is used to encrypt data across microservices.

4

Distributed tracing is a method for data backup in microservices.

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 ensures all nodes in a distributed system either commit or abort a transaction.

2

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

3

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

4

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

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

2

A load balancer is used to encrypt data before transmission.

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

2

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

3

Leader election ensures all nodes have the same processing speed.

4

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

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 about achieving the highest processing speed in a distributed system.

3

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

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

2

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

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 to a distributed database will eventually be reflected across all nodes.

2

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

3

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

4

Eventual consistency guarantees real-time data replication.

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

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1

Distributed caching is a security measure to prevent data breaches.

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 used to synchronize data between different databases.

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 file compression algorithm used in distributed systems.

3

MapReduce is a networking protocol for distributed systems.

4

MapReduce is a type of database management system.

How does a distributed file system differ from a traditional file system?

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1

A distributed file system spans multiple servers, whereas a traditional file system operates on a single machine.

2

A distributed file system is limited to single-user access.

3

A distributed file system stores files only temporarily.

4

A distributed file system does not provide file sharing capabilities.

What is the role of a distributed file system in distributed computing?

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1

A distributed file system stores data only in a local storage device.

2

A distributed file system is a system that only works on a single server.

3

A distributed file system is used solely for creating backups.

4

A distributed file system manages files across multiple servers, providing a unified view of data.

Explain the concept of sharding in distributed databases.

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1

Sharding in distributed databases is a technique for backup and recovery.

2

Sharding in distributed databases involves combining multiple databases into one large database.

3

Sharding in distributed databases is about encrypting data for security.

4

Sharding in distributed databases involves dividing the database into smaller, more manageable pieces called shards.

What are some of the key challenges with distributed databases?

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1

Key challenges with distributed databases include user interface design and usability.

2

Key challenges with distributed databases include limited storage capacity.

3

Key challenges with distributed databases include the cost of hardware and software.

4

Key challenges with distributed databases include consistency, latency, and fault tolerance.

What is the purpose of a distributed database?

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1

The purpose of a distributed database is to eliminate the need for data replication.

2

The purpose of a distributed database is to store all data on a single server for easy access.

3

The purpose of a distributed database is to manage data across multiple locations for improved performance and reliability.

4

The purpose of a distributed database is to reduce the need for data redundancy.

What is the difference between synchronous and asynchronous communication in distributed systems?

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1

Synchronous communication uses real-time processing, while asynchronous communication uses batch processing.

2

Synchronous communication in distributed systems requires all parties to wait for each other, while asynchronous communication allows them to proceed independently.

3

Synchronous communication is a feature of all distributed systems.

4

Synchronous communication is always faster than asynchronous communication.

Explain the CAP theorem and its implications for distributed systems.

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1

The CAP theorem states that a distributed system can achieve Consistency, Availability, and Partition tolerance at all times.

2

The CAP theorem states that a distributed system cannot simultaneously guarantee Consistency, Availability, and Partition tolerance.

3

The CAP theorem relates to achieving high performance in a single-node database.

4

The CAP theorem is about optimizing network bandwidth in distributed systems.

What is distributed computing, and why is it important?

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1

Distributed computing involves multiple computers working together to achieve a common goal.

2

Distributed computing is about enhancing the performance of a single processor.

3

Distributed computing refers to the use of a single computer for multiple tasks.

4

Distributed computing means using cloud services for all computing needs.