Computer Knowledge
Cloud Computing Management
2,340 Questions
Cloud computing management involves administering web based services, data storage, and network security protocols. This subject is heavily featured in the computer aptitude sections of banking and government recruitment tests. The practice questions address SaaS backup, cross region storage replication, and serverless application designs.
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Cloud Computing Management Questions
What is the term for a software platform that allows developers to build and deploy applications without managing the underlying infrastructure?
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Platform as a Service (PaaS)
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Infrastructure as a Service (IaaS)
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Software as a Service (SaaS)
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Cloud Computing
A
Correct answer
Explanation
Platform as a Service (PaaS) is a software platform that allows developers to build and deploy applications without managing the underlying infrastructure.
Which of the following is a type of cloud computing architecture?
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Multi-cloud
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Hybrid cloud
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Public cloud
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All of the above
D
Correct answer
Explanation
Multi-cloud, hybrid cloud, and public cloud are all types of cloud computing architectures.
What is the term for the ability of a cloud computing system to recover from failures and maintain availability?
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Reliability
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Availability
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Fault Tolerance
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All of the above
D
Correct answer
Explanation
Reliability, availability, and fault tolerance are all terms used to describe the ability of a cloud computing system to recover from failures and maintain availability.
Which of the following is a cloud computing pricing model?
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Pay-as-you-go
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Subscription
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Per-use
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All of the above
D
Correct answer
Explanation
Pay-as-you-go, subscription, and per-use are all cloud computing pricing models.
What is the term for the process of moving data, applications, or other business elements from a cloud-based environment to a traditional on-premises environment?
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Cloud Repatriation
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Cloud Migration
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Cloud Adoption
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Cloud Transformation
A
Correct answer
Explanation
Cloud Repatriation is the process of moving data, applications, or other business elements from a cloud-based environment to a traditional on-premises environment.
Which cloud computing platform is widely used for Big Data Analytics?
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Amazon Web Services (AWS)
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Microsoft Azure
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Google Cloud Platform (GCP)
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All of the above
D
Correct answer
Explanation
AWS, Azure, and GCP are leading cloud computing platforms that offer a wide range of services and tools specifically designed for Big Data Analytics. These platforms provide scalable, cost-effective, and secure environments for storing, processing, and analyzing large datasets.
Which technology is used for distributed processing of large datasets?
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Hadoop
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Spark
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Flink
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All of the above
D
Correct answer
Explanation
Hadoop, Spark, and Flink are popular technologies used for distributed processing of large datasets. Hadoop provides a framework for storing and processing data across clusters of computers. Spark is a fast and general-purpose engine for large-scale data processing. Flink is a stream processing framework designed for real-time applications.
Which type of mobile enterprise application is designed to provide real-time information to users?
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Transactional applications
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Analytical applications
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Collaborative applications
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Informational applications
D
Correct answer
Explanation
Informational applications are designed to provide real-time information to users, such as news updates, weather forecasts, and stock market data.
What is the key factor to consider when choosing a mobile enterprise application development platform?
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Cost
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Features
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Security
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All of the above
D
Correct answer
Explanation
When choosing a mobile enterprise application development platform, it is important to consider cost, features, security, and other factors to ensure the platform meets the specific needs of the organization.
Which of the following is NOT a common mobile enterprise application deployment model?
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On-premises deployment
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Cloud deployment
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Hybrid deployment
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SaaS deployment
D
Correct answer
Explanation
While on-premises deployment, cloud deployment, and hybrid deployment are common mobile enterprise application deployment models, SaaS (Software as a Service) deployment is not typically included in this category.
Which of the following is NOT a common architectural style?
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Client-Server
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Microservices
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Layered Architecture
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Event-Driven Architecture
C
Correct answer
Explanation
Layered Architecture is not an architectural style; it is a structural pattern that describes how a system can be organized into layers.
Which of the following is a common big data technology used in systems engineering?
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Hadoop
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Spark
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NoSQL databases
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All of the above
D
Correct answer
Explanation
Hadoop, Spark, and NoSQL databases are widely used big data technologies that offer scalable and efficient solutions for data storage, processing, and analysis in systems engineering.
How can big data contribute to improving system reliability?
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By identifying potential failure modes
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By optimizing system maintenance schedules
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By predicting system performance under various conditions
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All of the above
D
Correct answer
Explanation
Big data can contribute to improving system reliability by enabling the identification of potential failure modes, optimization of system maintenance schedules, prediction of system performance under various conditions, and root cause analysis of system failures.
Which of the following is a key consideration in selecting big data technologies for systems engineering?
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Scalability
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Performance
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Cost-effectiveness
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All of the above
D
Correct answer
Explanation
When selecting big data technologies for systems engineering, it is essential to consider factors such as scalability, performance, cost-effectiveness, ease of use, and integration with existing systems.
How can big data analytics contribute to optimizing system performance?
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By identifying system bottlenecks
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By optimizing system resource allocation
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By predicting system behavior under various conditions
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All of the above
D
Correct answer
Explanation
Big data analytics can contribute to optimizing system performance by enabling the identification of system bottlenecks, optimization of system resource allocation, prediction of system behavior under various conditions, and identification of potential performance improvements.