44–66
According to sources, the most probable answer to this query is 44–66 point size.
This is because the eyes and the illustration should match the proportion of distance and height factors.
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Answer:
Blocking Mode
Explanation:
Spanning Tree Protocol is used to allow path redundancy in the network without creating cycles/circles also called loops.
When two parts of the switched network are connected via two or more Layer 2 switches this result in a loop.
This affects the performance of the network as the result of broadcast packets flooding.
STP puts one port of the switch to forwarding mode and the rest of the ports within the same part of the network to the blocking mode to avoid broadcast packet flooding. STP puts all the ports that are allowing redundant paths to blocking mode and the one port that is left after this is placed in forward mode.
Spanning Tree Algorithm is used by STP to determine the optimal path of switch to the network.
Bridge Protocol Data Units are used to share the information about the optimal path determined by the spanning tree algorithm with other switches.
This information helps STP to eliminate the redundant paths.
So this is how STP allows only one active path to the destination while blocking all other paths to avoid switching loop.
Answer:
https://www.python.org/about/gettingstarted/
Explanation:
its a site i used
Answer:
The answer of the following question is Brute force attack
.
Explanation:
A brute force attack is the error and trial method that is used by an application program to decode the encrypted data like passwords or the Data Encryption Standard (DES) keys, which through an exhaustive effort (by using brute force) rather than the employing an intellectual strategies.
Answer:
4. Supervised learning.
Explanation:
Supervised and Unsupervised learning are both learning approaches in machine learning. In other words, they are sub-branches in machine learning.
In supervised learning, an algorithm(a function) is used to map input(s) to output(s). The aim of supervised learning is to predict output variables for given input data using a mapping function. When an input is given, predictions can be made to get the output.
Unsupervised learning on the other hand is suitable when no output variables are needed. The only data needed are the inputs. In this type of learning, the system just keeps learning more about the inputs.
Special applications of supervised learning are in image recognition, speech recognition, financial analysis, neural networking, forecasting and a whole lot more.
Application of unsupervised learning is in pre-processing of data during exploratory analysis.
<em>Hope this helps!</em>
