The Power of ML/AI for Data Analytics in the Cloud

BE.

The two main advantages of using machine learning (ML)/artificial intelligence (AI) for data analytics in the cloud as opposed to on-premises are:

B. Elasticity allows access to a large pool of compute resources. One of the main advantages of using ML/AI for data analytics in the cloud is the elastic nature of cloud computing resources. Elasticity allows the user to scale up or down the amount of computing resources needed, depending on the current demand for processing power. This means that cloud users can access a larger pool of computing resources in the cloud, which can be used for running complex ML/AI algorithms and processing large datasets. Additionally, the cloud providers can provide auto-scaling features that automatically scale the resources up and down based on the demand, which helps to optimize cost while still delivering the required performance.

E. A pay-as-you-go approach allows the company to save money. Another advantage of using ML/AI for data analytics in the cloud is the pay-as-you-go pricing model offered by most cloud providers. This model allows the company to pay only for the computing resources they use, which helps to reduce overall costs. The pay-as-you-go model also provides greater flexibility, as the company can easily adjust their usage according to their budget and demand.

Option A, C, D, and F are incorrect:

A. Cloud providers offer enhanced technical support. While it's true that cloud providers offer technical support, it is not necessarily an advantage of using ML/AI for data analytics in the cloud. Technical support is a general feature of cloud computing and can be applied to any type of workload.

C. The shared responsibility model offers greater security. The shared responsibility model refers to the division of security responsibilities between the cloud provider and the user. While this is important for overall security, it is not a specific advantage of using ML/AI for data analytics in the cloud.

D. AI enables DevOps to build applications easier and faster. While AI can help with building applications, it is not a specific advantage of using ML/AI for data analytics in the cloud. Additionally, this option is a repetition of option F.

F. ML enables DevOps to build applications easier and faster. While ML can help with building applications, it is not a specific advantage of using ML/AI for data analytics in the cloud. Additionally, this option is a repetition of option D.