Answer:
DNS translates domain names and hostnames into IP addresses through a basic lookup function. When a user requests a domain name (as in the case of a browser lookup) the computer queries the local DNS server to find the matching IP address. If the local DNS server does not contain the information in it's local database, it contacts the next higher DNS system or the root domain DNS servers (i.e. microsoft.com - if the local system does not know it, it will query the well-known DNS server for the .com domain - which will know where to get the information).
DNS helps the users by not requiring them to remember the IP address of every system them want to connect with. microsoft.com is much easier to remember than 40.82.167.220.
DNS also helps the internet scale through the use of load balancing. Multiple systems can comprise a single web site with the web pages stored on many different machines with multiple IP addresses. When a user requests the microsoft.com website IP, they are given the primary IP of the load balancer sitting in front of the multiple computers. The load balancer will then distribute the traffic to any of the systems that are hosting the web page that are not busy. To the end user it look like they are connecting to a single machine - when in fact they are connecting to one of potentially hundreds of web servers with the same content. As traffic grows on the internet more servers are necessary to handle the additional traffic. DNS helps ensure the end user will connect to the proper web server regardless of the number of web server copies spun up in response to the additional traffic. This allows the capacity to scale as necessary without interruption to the web site.
Explanation:
It depends on the heater but in most case it works pretty well
I believe the answer is customer service
Answer:
B. reducing multi-language communication friction in a company through automatic language translation.
Explanation:
Deep learning is a branch of artifical intelligence whereby computer systems are programmed to operate in such a way they imitates tbe functioning of the human brain. Hence, emplyins network of neurons structures to learn, gain insight and make predictions. Using layers of network architectures, deep learning in predictive, including facial recognition, virtual assistants, language translation and many more. Deep learning is able to create value not by abrogating humans from the work fold but by enhancing and bridging the gap in areas which need the leverage of technology such as language translation as it enables individuals with different language prowee to be able to communicate through automatic translation.
