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NEW QUESTION: 1
Which WAN technology is a cost-effective method to deliver 100Mb of bandwidth to multiple branch offices?
A. Metro Ethernet
B. DSL
C. DWDM
D. ISDN
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Metro Ethernet
Metro Ethernet uses well-known "Ethernet" to deliver low-cost and high-speed MAN/WAN connectivity for organizations. Many service providers now offer Metro Ethernet solutions to deliver a wide range of converged network services such as data, voice, and video on the same wire. Metro Ethernet provides enterprise LAN type functionality out in the MAN and WAN. increasing the throughput available for applications. Metro Ethernet bandwidths can range from 10Mbps to 1 Gbps. and even higher in some cases, allowing for support for higher performance and increased QoS requirements. In contrast to the rigid nature of traditional TDM provisioning, Metro Ethernet services are much easier to deploy and scale due to the flexible bandwidth increments. Metro Ethernet technology is appealing to many customers because they are already comfortable using Ethernet throughout their LAN environments.

NEW QUESTION: 2
HOTSPOT





Answer:
Explanation:

* setData method (dataTransfer)
Specifies the data and its format for the current drag-and-drop operation.
* getData method (dataTransfer)
Retrieves the specified formatted data from the current drag-and-drop operation.

NEW QUESTION: 3
Why do you need to split a machine learning dataset into training data and test data?
A. So you can try two different sets of features
B. To make sure your model is generalized for more than just the training data
C. To allow you to create unit tests in your code
D. So you can use one dataset for a wide model and one for a deep model
Answer: B
Explanation:
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.
Reference: https://machinelearningmastery.com/a-simple-intuition-for-overfitting/