Answer:
Option b. Choke point
Explanation:
In firewall, a choke point can be defined as a single point which allows all of the network traffic including incoming and outgoing to be funneled. As the passage of the whole traffic through the choke point regulates the flow as it focus monitoring and controls bandwidth consumption, provides authentication, control efforts like Internet firewalls.
Answer:
Redundant paths can be available without causing logical Layer 2 loops.
Explanation:
- Spanning Tree Protocol is used to allow redundancy in the Layer 2 switched networks without creating cycles/circles also called loops.
- These loops are called physical 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 forward 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 block 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 tracks all the links in the switched network and eliminates redundant loops by allowing only one active path to the destination while blocking all other paths.
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>
Answer:
Windows 8.1 Core
Explanation:
In this particular example, we're going to use Windows 8.1 Core, is the most basic of the window's family, in this case, only we need an OS to connect the hardware with the cloud computing for security and is not necessary another license, in addition, Windows 8.1 core is easiest to use, is so friendly with the user.
<span>Act on your emotions in the moment instead of thinking about them. </span>
