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Type 1 and Type 2 hypervisors are virtualization technologies used to manage virtual machines. 1. Type 1 Hypervisor: Often referred to as a "bare-metal" hypervisor, it runs directly on hardware without relying on a host operating system. This setup minimizes overhead, enhancing performance and efficiency. 2. Type 2 Hypervisor: Operates on top of a host operating system, adding a layer of abstraction. While easier to set up, it introduces additional overhead, making it less efficient than Type 1 hypervisors. 3. Use Cases: Type 1 hypervisors are commonly used in enterprise data centers for large-scale virtualization, whereas Type 2 hypervisors are suitable for smaller-scale testing and development environments. The distinction lies in their operational layers, making Type 1 hypervisors more suitable for performance-intensive environments. Why Other Options Are Incorrect: • B) Containers usage: Hypervisors manage VMs, not containers. Containers use the host OS kernel. • C) Memory usage: Both types of hypervisors depend on workload, not intrinsic memory requirements. • D) Support for VMs: Both types support multiple VMs; this is not exclusive to either. • E) Security: Type 1 hypervisors are generally considered more secure due to direct hardware access.
Which time series application would most likely require ARIMA modeling for accurate forecasting?
Which of the following statements correctly differentiates between continuous and categorical data?
In SQL, which type of JOIN will return all rows from the left table and the matching rows from the right table, filling with NULLs where there is no match?
To help a retail business increase its conversion rate, a data analyst should start by defining which of the following metrics?
In Huffman Coding, which property ensures that no code is a prefix of another?
Which of the following best describes the primary role of a data analyst within an organization?
Which of the following is the best example of metadata in a database system?
Which data collection method is best suited for obtaining real-time data from a third-party application?
When integrating multiple datasets, which approach helps resolve inconsistencies and create uniformity across all data sources?
If a dataset has a mean significantly higher than the median, which of the following is most likely true?
