Answer:
Load balancing is the mechanism happening between the different components to achieve the aim independently in the server. They are not aware of the presence of the other resources with them.
Clustering is the process in which the components work to gain the desired results in the form of group.They work in group so that there are no chances of crash in server.
The similarity arises between these two process are :
- They have capability to work even after failure of system
- Can have unnecessary access to the information
- Scalable
the answer is they can store more information
Answer:
Check Explanation (last paragraph, please).
Explanation:
The acronym "XML" simply stand for Extensible Markup Language and it is a programming language that is for coding infomation or data.
Extensible Markup Language(XML) is very acceptable and whenever one read or search a website that uses XML, it gives viewers good experience.
Piers can use XML to improve his website creations through the declaration of a namespace which has an integral part to a script element. The tags in XML does not have limitation like other languages. Also, Extensible Markup Language(XML) does not need to be updated all the time in as much as the website itself is being updated.
Answer:
mystr = input("Enter a string ")
length = len(mystr)
while length<10:
mystr = input("Enter a string ")
length = len(mystr)
if(length>=10):
break
if len(mystr)%2==0:
print(mystr.lower())
else:
print(mystr.upper())
Explanation:
The variable mystr is used to save user's input which is received with the input function
A second variable length is used to save the length of the input string Using a while statement the user is continually prompted to enter a string while length is less than 10.
If length is greater or equal to 10. We check for even or odd using the modulo (%) operator.
We use lower() and upper() to change the case of the string
Answer:
The answer is nearest-neighbor learning.
because nearest neighbor learning is classification algorithm.
It is used to identify the sample points that are separated into different classes and to predict that the new sample point belongs to which class.
it classify the new sample point based on the distance.
for example if there are two sample points say square and circle and we assume some center point initially for square and circle and all the other points are added to the either square or circle cluster based on the distance between sample point and center point.
while the goal of decision tree is to predict the value of the target variable by learning some rules that are inferred from the features.
In decision tree training data set is given and we need to predict output of the target variable.
Explanation:
