Drawback of Distributed Computing MCQs. 100 Best Multiple Choice Question covering topics – Network Communication Overheads. Data Consistency Challenges. Fault Tolerance Challenges. Security Challenges. Scalability Limitations. Distributed Algorithms. Load Balancing. Resource Management Challenges. Heterogeneity and Compatibility Challenges.
Drawback of Distributed Computing MCQs
Set 1: Network Communication Overheads – Drawback of Distributed Computing
1. Which of the following is a major overhead in distributed computing due to network communication?
(a) Data replication
(b) Load balancing
(c) Latency
(d) Fault tolerance
2. The increased message traffic in distributed systems can lead to:
(a) Improved performance
(b) Reduced bandwidth consumption
(c) Network congestion
(d) Enhanced security
3. Which technique aims to minimize network communication overheads by reducing the amount of data transmitted?
(a) Data compression
(b) Caching
(c) Load balancing
(d) Encryption
4. In distributed systems, the time it takes for a message to travel from sender to receiver is known as:
(a) Bandwidth
(b) Throughput
(c) Latency
(d) Jitter
5. Which factor can significantly impact network communication latency in geographically distributed systems?
(a) Processor speed
(b) Memory capacity
(c) Disk I/O
(d) Distance between nodes
6. The variation in network latency, causing inconsistent message delivery times, is called:
(a) Packet loss
(b) Jitter
(c) Round-trip time
(d) Bandwidth
7. Which communication protocol is often used in distributed systems due to its connectionless nature and low overhead?
(a) TCP
(b) UDP
(c) FTP
(d) HTTP
8. To mitigate network communication overheads, distributed systems often employ:
(a) Centralized architectures
(b) Synchronous communication
(c) Asynchronous communication
(d) Tightly coupled components
9. Which approach involves executing multiple tasks concurrently to improve system responsiveness despite network delays?
(a) Serial processing
(b) Parallel processing
(c) Batch processing
(d) Transaction processing
10. The practice of storing copies of data on multiple nodes to reduce network traffic and improve access times is known as:
(a) Caching
(b) Load balancing
(c) Data replication
(d) Data partitioning
Set 2: Data Consistency Challenges – Drawback of Distributed Computing
11. In distributed systems, maintaining consistency of replicated data across different nodes is crucial. This challenge is known as:
(a) Data partitioning
(b) Data replication
(c) Data consistency
(d) Data serialization
12. Which of the following is NOT a common technique for ensuring data consistency in distributed systems?
(a) Two-phase commit protocol (2PC)
(b) Quorum consensus
(c) Vector clocks
(d) Random access memory (RAM)
13. The CAP theorem states that distributed systems can only guarantee two out of three properties. What are these properties?
(a) Confidentiality, Availability, Partition tolerance
(b) Consistency, Availability, Partition tolerance
(c) Consistency, Atomicity, Partition tolerance
(d) Consistency, Availability, Performance
14. In distributed systems, the phenomenon where a node receives conflicting updates for the same data item is called:
(a) Write conflict
(b) Read conflict
(c) Update conflict
(d) Data conflict
15. Which consistency model guarantees that all reads will see the most recent write, even in the presence of network partitions?
(a) Strong consistency
(b) Eventual consistency
(c) Causal consistency
(d) Weak consistency
16. Eventual consistency is a relaxed consistency model where:
(a) All writes are immediately visible to all nodes.
(b) Updates are propagated asynchronously, and conflicts are resolved later.
(c) Updates are applied in a strict, sequential order.
(d) Reads are guaranteed to return the most recent write.
17. Which technique can be used to detect and resolve write conflicts in eventually consistent systems?
(a) Two-phase commit (2PC)
(b) Version vectors
(c) Quorum consensus
(d) Data replication
18. In a distributed database, which protocol ensures that a transaction either commits successfully on all nodes or fails on all nodes?
(a) Two-phase commit protocol (2PC)
(b) Three-phase commit protocol (3PC)
(c) Paxos consensus
(d) Raft consensus
19. The process of synchronizing clocks across different nodes in a distributed system to maintain a consistent view of time is called:
(a) Clock synchronization
(b) Time synchronization
(c) Distributed synchronization
(d) Network synchronization
20. Which of the following is NOT a common clock synchronization algorithm used in distributed systems?
(a) Network Time Protocol (NTP)
(b) Berkeley Algorithm
(c) Cristian’s Algorithm
(d) Lamport’s Algorithm
Set 3: Fault Tolerance Challenges – Drawback of Distributed Computing
21. The ability of a distributed system to continue operating correctly despite the failure of one or more components is known as:
(a) Reliability
(b) Availability
(c) Scalability
(d) Fault tolerance
22. Which of the following is NOT a common cause of failure in distributed systems?
(a) Network partitions
(b) Node crashes
(c) Data inconsistency
(d) Software bugs
23. A system that can automatically detect and recover from failures without human intervention is called:
(a) Self-healing
(b) Self-managing
(c) Self-organizing
(d) Self-replicating
24. The technique of replicating data and computations across multiple nodes to ensure availability in case of failures is called:
(a) Load balancing
(b) Data partitioning
(c) Redundancy
(d) Caching
25. In distributed systems, a group of processes that cooperate to provide a service even if some processes fail is called:
(a) Cluster
(b) Consensus group
(c) Replica set
(d) Distributed group
26. Which consensus algorithm is often used in distributed systems to ensure agreement on a single value among a group of nodes?
(a) Two-phase commit (2PC)
(b) Paxos
(c) Raft
(d) Both (b) and (c)
27. The time it takes for a system to recover from a failure and resume normal operation is known as:
(a) Mean time between failures (MTBF)
(b) Mean time to repair (MTTR)
(c) Recovery time objective (RTO)
(d) Recovery point objective (RPO)
28. A checkpoint is a technique used in fault tolerance to:
(a) Detect failures
(b) Prevent failures
(c) Save the system state periodically
(d) Roll back transactions
29. Which fault tolerance technique involves periodically sending “heartbeat” messages between nodes to detect failures?
(a) Failure detection
(b) Failure masking
(c) Failure recovery
(d) Failure prevention
30. Which type of failure occurs when a node in a distributed system stops responding but is still running?
(a) Crash failure
(b) Omission failure
(c) Byzantine failure
(d) Network partition
Set 4: Security Challenges – Drawback of Distributed Computing
31. Which of the following is NOT a primary security concern in distributed systems?
(a) Confidentiality
(b) Integrity
(c) Availability
(d) Scalability
32. The practice of disguising data to protect it from unauthorized access is known as:
(a) Authentication
(b) Authorization
(c) Encryption
(d) Auditing
33. In a distributed system, which type of attack involves intercepting and modifying messages between nodes?
(a) Denial-of-service (DoS) attack
(b) Man-in-the-middle (MitM) attack
(c) Phishing attack
(d) SQL injection attack
34. Which security mechanism verifies the identity of a user or process attempting to access a resource?
(a) Encryption
(b) Authorization
(c) Authentication
(d) Auditing
35. The process of granting or denying access to resources based on user roles or permissions is called:
(a) Encryption
(b) Authentication
(c) Authorization
(d) Auditing
36. A digital signature is used to:
(a) Encrypt data
(b) Verify the authenticity and integrity of a message
(c) Control access to resources
(d) Hide the identity of the sender
37. Which security attack involves overwhelming a system with excessive requests to make it unavailable to legitimate users?
(a) Man-in-the-middle (MitM) attack
(b) Phishing attack
(c) Denial-of-service (DoS) attack
(d) SQL injection attack
38. A firewall is a security measure that:
(a) Encrypts data
(b) Authenticates users
(c) Controls network traffic based on predefined rules
(d) Detects intrusions
39. Which of the following is NOT a common cryptographic algorithm used in distributed systems?
(a) AES
(b) RSA
(c) DES
(d) HTML
40. In a public key infrastructure (PKI), a certificate authority (CA) is responsible for:
(a) Generating and distributing private keys
(b) Issuing and managing digital certificates
(c) Encrypting data
(d) Authenticating users
Set 5: Scalability Limitations – Drawback of Distributed Computing
41. Scalability refers to the ability of a distributed system to:
(a) Handle a growing amount of work
(b) Maintain data consistency
(c) Recover from failures
(d) Secure data
42. Which of the following is NOT a common scaling strategy in distributed systems?
(a) Vertical scaling
(b) Horizontal scaling
(c) Data partitioning
(d) Encryption
43. Vertical scaling involves:
(a) Adding more nodes to the system
(b) Upgrading the hardware of existing nodes
(c) Dividing data across multiple nodes
(d) Replicating data across multiple nodes
44. Horizontal scaling involves:
(a) Upgrading the hardware of existing nodes
(b) Adding more nodes to the system
(c) Replicating data across multiple nodes
(d) Dividing data across multiple nodes
45. Which scaling strategy is generally more cost-effective in the long run?
(a) Vertical scaling
(b) Horizontal scaling
46. A bottleneck in a distributed system is:
(a) A component that limits the overall performance
(b) A security vulnerability
(c) A data consistency issue
(d) A fault tolerance mechanism
47. Amdahl’s Law describes the theoretical speedup of a system when only a portion of the system can be parallelized. It highlights the limitations of:
(a) Vertical scaling
(b) Horizontal scaling
(c) Data partitioning
(d) Load balancing
48. In distributed systems, the overhead of coordinating tasks among multiple nodes is known as:
(a) Communication overhead
(b) Coordination overhead
(c) Synchronization overhead
(d) Management overhead
49. Which technique involves breaking down a large dataset into smaller chunks and distributing them across multiple nodes for parallel processing?
(a) Load balancing
(b) Data partitioning
(c) Data replication
(d) Caching
50. Which of the following is NOT a common challenge in scaling distributed systems?
(a) Data consistency
(b) Fault tolerance
(c) Security
(d) Centralization
Set 6: Distributed Algorithms – Drawback of Distributed Computing
51. In distributed systems, algorithms that coordinate the actions of multiple nodes to achieve a common goal are called:
(a) Centralized algorithms
(b) Parallel algorithms
(c) Distributed algorithms
(d) Sequential algorithms
52. Which distributed algorithm elects a single leader among a group of nodes?
(a) Paxos
(b) Raft
(c) Bully algorithm
(d) All of the above
53. The Paxos and Raft algorithms are primarily designed for:
(a) Load balancing
(b) Data partitioning
(c) Consensus
(d) Failure detection
54. Which distributed algorithm finds the shortest path between two nodes in a network?
(a) Dijkstra’s algorithm
(b) Bellman-Ford algorithm
(c) Floyd-Warshall algorithm
(d) All of the above
55. In distributed systems, the problem of determining the global state of the system at a particular point in time is known as:
(a) Global state snapshot
(b) Snapshot isolation
(c) Distributed snapshot
(d) Consistent snapshot
56. Which algorithm is used to synchronize logical clocks in distributed systems?
(a) Lamport’s logical clocks
(b) Vector clocks
(c) Physical clocks
(d) Both (a) and (b)
57. The Chandy-Lamport algorithm is used for:
(a) Leader election
(b) Consensus
(c) Global state snapshot
(d) Distributed mutual exclusion
58. A distributed deadlock occurs when:
(a) A single process is waiting for a resource held by itself.
(b) Two or more processes are waiting for resources held by each other.
(c) A process is waiting for a resource that is not available.
(d) A process terminates unexpectedly.
59. Which algorithm is used to detect distributed deadlocks?
(a) Dijkstra’s algorithm
(b) Chandy-Misra-Haas algorithm
(c) Lamport’s logical clocks
(d) Vector clocks
60. Which algorithm is used to prevent distributed deadlocks?
(a) Wait-die
(b) Wound-wait
(c) Banker’s algorithm
(d) Both (a) and (b)
Set 7: Load Balancing – Drawback of Distributed Computing
61. Load balancing in distributed systems aims to:
(a) Improve fault tolerance
(b) Enhance security
(c) Distribute workload evenly across nodes
(d) Ensure data consistency
62. Which of the following is NOT a common load balancing algorithm?
(a) Round robin
(b) Least connections
(c) Weighted round robin
(d) Two-phase commit
63. In round robin load balancing, requests are:
(a) Distributed randomly to nodes
(b) Sent to the node with the least load
(c) Sent to nodes in a fixed, cyclical order
(d) Sent to nodes based on their weights
64. Least connections load balancing sends requests to:
(a) The node with the fewest active connections
(b) The node with the most active connections
(c) Nodes in a fixed, cyclical order
(d) Nodes based on their weights
65. Weighted round robin load balancing assigns weights to nodes, indicating:
(a) The priority of each node
(b) The processing capacity of each node
(c) The number of requests each node can handle
(d) All of the above
66. In distributed systems, a load balancer typically operates at which layer of the OSI model?
(a) Application layer (Layer 7)
(b) Transport layer (Layer 4)
(c) Network layer (Layer 3)
(d) Data link layer (Layer 2)
67. A load balancer that understands and interprets application-level protocols (like HTTP) is called:
(a) Layer 4 load balancer
(b) Layer 7 load balancer
(c) Network load balancer
(d) Hardware load balancer
68. Which type of load balancer is more intelligent and can make routing decisions based on application data?
(a) Layer 4 load balancer
(b) Layer 7 load balancer
69. Which of the following is NOT a benefit of load balancing in distributed systems?
(a) Improved performance
(b) Increased availability
(c) Enhanced security
(d) Scalability
70. A common technique for implementing load balancing in cloud environments is:
(a) Round robin DNS
(b) Reverse proxy
(c) Elastic Load Balancing (ELB)
(d) All of the above
Set 8: Resource Management Challenges – Drawback of Distributed Computing
71. In distributed systems, resource management involves:
(a) Allocating and scheduling resources efficiently
(b) Ensuring data consistency
(c) Detecting and recovering from failures
(d) Securing data
72. Resources in a distributed system can include:
(a) CPU time
(b) Memory
(c) Storage
(d) Network bandwidth
(e) All of the above
73. Which of the following is NOT a common challenge in resource management in distributed systems?
(a) Heterogeneity of resources
(b) Dynamic workload fluctuations
(c) Data consistency
(d) Competition for resources
74. Deadlock is a situation where:
(a) A process is waiting for a resource held by itself.
(b) Two or more processes are waiting for resources held by each other.
(c) A process is waiting for a resource that is not available.
(d) A process terminates unexpectedly.
75. Starvation occurs when:
(a) A process is waiting for a resource held by itself.
(b) Two or more processes are waiting for resources held by each other.
(c) A process is continuously denied access to a resource it needs.
(d) A process terminates unexpectedly.
76. Which resource management algorithm dynamically allocates resources based on current demand?
(a) First-come, first-served (FCFS)
(b) Shortest job first (SJF)
(c) Priority scheduling
(d) Dynamic resource allocation
77. Which technique involves temporarily moving a process from one node to another to balance the load?
(a) Load balancing
(b) Process migration
(c) Process scheduling
(d) Resource allocation
78. In distributed systems, the problem of allocating resources fairly among competing processes is known as:
(a) Fair scheduling
(b) Resource fairness
(c) Load balancing
(d) Resource allocation
79. Which of the following is NOT a common metric for evaluating resource utilization in distributed systems?
(a) CPU utilization
(b) Memory utilization
(c) Network bandwidth utilization
(d) Data consistency
80. Which cloud computing service model provides users with access to virtualized computing resources (e.g., servers, storage, networks) on demand?
(a) Infrastructure as a Service (IaaS)
(b) Platform as a Service (PaaS)
(c) Software as a Service (SaaS)
(d) Function as a Service (FaaS)
Set 9: Heterogeneity and Compatibility Challenges – Drawback of Distributed Computing
81. Heterogeneity in distributed systems refers to:
(a) Nodes having different hardware and software configurations
(b) Nodes being geographically dispersed
(c) Nodes having different roles and responsibilities
(d) All of the above
82. Which of the following is NOT a common challenge caused by heterogeneity in distributed systems?
(a) Communication protocol mismatch
(b) Data format incompatibility
(c) Load balancing
(d) Software library version conflicts
83. Middleware is a software layer that:
(a) Provides a uniform interface to hide the heterogeneity of underlying systems
(b) Manages network communication between nodes
(c) Ensures data consistency
(d) All of the above
84. Which middleware technology allows applications written in different programming languages to communicate with each other?
(a) Remote Procedure Call (RPC)
(b) Message-Oriented Middleware (MOM)
(c) Common Object Request Broker Architecture (CORBA)
(d) All of the above
85. In distributed systems, the problem of translating data from one format to another is known as:
(a) Data transformation
(b) Data translation
(c) Data conversion
(d) All of the above
86. Which of the following is a common data serialization format used in distributed systems?
(a) JSON
(b) XML
(c) Protocol Buffers
(d) All of the above
87. The process of automatically discovering and managing services in a distributed environment is called:
(a) Service discovery
(b) Service registry
(c) Service broker
(d) Service mesh
88. Which technology provides a way to package and deploy applications in a standardized format across different environments?
(a) Virtual machines (VMs)
(b) Containers (e.g., Docker)
(c) Serverless functions
(d) All of the above
89. Which of the following is NOT a benefit of using containers in distributed systems?
(a) Portability
(b) Improved resource utilization
(c) Faster deployment
(d) Increased attack surface
90. Which technology creates a dedicated network layer for service-to-service communication in a microservices architecture?
(a) API gateway
(b) Service mesh
(c) Load balancer
(d) Message queue
Set 10: Distributed Transactions and Concurrency – Drawback of Distributed Computing
91. A distributed transaction is:
(a) A transaction that spans multiple databases
(b) A transaction that involves multiple users
(c) A transaction that runs on a single node
(d) A transaction that cannot be rolled back
92. ACID properties are essential for ensuring the reliability of transactions. ACID stands for:
(a) Atomicity, Consistency, Isolation, Durability
(b) Availability, Consistency, Isolation, Durability
(c) Atomicity, Consistency, Integrity, Durability
(d) Atomicity, Confidentiality, Isolation, Durability
93. Atomicity in a transaction means that:
(a) The transaction must be executed in its entirety or not at all.
(b) The transaction must leave the database in a consistent state.
(c) The transaction must be isolated from other transactions.
(d) The transaction must be durable once committed.
94. The two-phase commit (2PC) protocol is used to:
(a) Ensure data consistency in distributed databases
(b) Coordinate the commitment of a distributed transaction
(c) Handle failures in distributed systems
(d) All of the above
95. Concurrency control in distributed databases is necessary to:
(a) Prevent deadlocks
(b) Ensure serializability of transactions
(c) Manage access to shared resources
(d) All of the above
96. Which of the following is NOT a common concurrency control technique in distributed databases?
(a) Locking
(b) Timestamp ordering
(c) Optimistic concurrency control
(d) Round robin
97. In distributed databases, a phantom read occurs when:
(a) A transaction reads a data item twice, and the value changes between reads.
(b) A transaction reads a data item that was previously deleted by another transaction.
(c) A transaction reads a set of data items, and new items are added to the set by another transaction.
(d) A transaction reads a data item that was previously updated by another transaction.
98. The Paxos algorithm is used for:
(a) Distributed transaction management
(b) Concurrency control
(c) Consensus
(d) Failure detection
99. In distributed databases, the BASE properties are sometimes used as an alternative to ACID. BASE stands for:
(a) Basically Available, Soft state, Eventual consistency
(b) Basically Available, Strong state, Eventual consistency
(c) Basically Available, Soft state, Early consistency
(d) Basically Available, Strong state, Early consistency
100. Which of the following is NOT a challenge in implementing distributed transactions?
(a) Network latency
(b) Partial failures
(c) Scalability
(d) Data encryption
Read Also: Quantum Computing MCQs
Main overhead in distributed systems?
Network latency, impacting performance.
CAP theorem’s impact on system design?
Forces trade-offs between consistency, availability, and partition tolerance.
Key data consistency challenges?
Handling write conflicts, ensuring data visibility, choosing consistency models.
Why is fault tolerance crucial, and how is it achieved?
Failures are inevitable; redundancy, consensus algorithms, failure detection help.
Security risks and mitigation in distributed systems?
Risks: unauthorized access, data breaches, attacks. Mitigations: encryption, authentication, authorization, firewalls.
Scaling limitations and strategies?
Bottlenecks, overhead, consistency issues. Solutions: horizontal scaling, data partitioning, load balancing.
Resource management complexities and potential issues?
Challenges: heterogeneity, dynamic workloads, competition. Issues: deadlocks, starvation.
Heterogeneity’s impact and solutions?
Communication, data format issues. Solutions: middleware, serialization, containers.
Distributed algorithms – why essential?
Coordinate actions across nodes. Crucial for leader election, consensus, snapshots, deadlock handling.
Challenges in distributed transactions vs. traditional ones?
Network latency, partial failures, ensuring ACID properties across databases. Increased complexity due to coordination.
Quick Revision – Drawback of Distributed Computing
| Topic | Key Concepts and Challenges |
| Network Communication Overheads | Latency, network congestion, data compression, asynchronous communication, data replication |
| Data Consistency Challenges | CAP theorem, consistency models (strong, eventual), write conflicts, two-phase commit, clock synchronization |
| Fault Tolerance Challenges | Fault tolerance, redundancy, consensus algorithms (Paxos, Raft), failure detection, recovery time |
| Security Challenges | Confidentiality, integrity, availability, encryption, authentication, authorization, firewalls |
| Scalability Limitations | Vertical vs. horizontal scaling, bottlenecks, Amdahl’s Law, data partitioning, load balancing |
| Distributed Algorithms | Leader election, consensus, shortest path, global state snapshot, logical clocks, deadlock detection/prevention |
| Load Balancing | Round robin, least connections, weighted round robin, layer 4 vs. layer 7 load balancers |
| Resource Management Challenges | Resource allocation, scheduling, deadlocks, starvation, process migration, resource fairness |
| Heterogeneity and Compatibility Challenges | Middleware, data serialization, service discovery, containers, service mesh |
| Distributed Transactions and Concurrency | ACID properties, two-phase commit, concurrency control, phantom reads, BASE properties |