I don’t see any of “the following numbers”
Answer:
The answer to this question as follows:
1) False
2) False
3) True
Explanation:
The description of the above option as follows
- In option 1, A single character variable must be contained in one quote mark, but it is based on the alphabet, which is a specific device, and the price of a continued character varies from one device to another, that's why it is false.
- In option 2, This option is wrong because in assembly language the identifier value must not exceed the length than 247 characters.
- In option 3, It is correct because in the variable declaration the first char should be a letter, _, @ or $letter. A total of 1-247 characters. The default case is insensitive.
Answer:
Given address = 94EA6
tag = 0 * 94 ( 10010100 )
line = 0 * 1 D 4 ( 111010100 )
word position = 0*6 ( 110 )
Explanation:
using the direct mapping method
Number of lines = 512
block size = 8 words
word offset =
= 3 bit
index bit =
= 9 bit
Tag = 20 - ( index bit + word offset ) = 20 - ( 3+9) = 8 bit
Given address = 94EA6
tag = 0 * 94 ( 10010100 )
line = 0 * 1 D 4 ( 111010100 )
word position = 0*6 ( 110 )
Answer:
theSentence = input('Enter sentence: ')
theSentence = theSentence.split()
sentence_split_list =[]
for word in theSentence:
sentence_split_list.append(word[1:]+word[0]+'ay')
sentence_split_list = ' '.join(sentence_split_list)
print(sentence_split_list)
Explanation:
Using the input function in python Programming language, the user is prompted to enter a sentence. The sentence is splited and and a new list is created with this statements;
theSentence = theSentence.split()
sentence_split_list =[ ]
In this way every word in the sentence becomes an element in this list and individual operations can be carried out on them
Using the append method and list slicing in the for loop, every word in the sentence is converted to a PIG LATIN
The attached screenshot shows the code and output.
Answer:
The answer is "using validation error".
Explanation:
The validation error is used to response the test for one of the queries is activated to the participant, which may not properly answer the question. These errors go up continuously after each time, the processing rate is too high and also the method is different.
- These errors are also unless to increase when they are actually in the problem.
- The training level will be that, if the learning error may not increase when the model overrides the learning set and you should stop practicing.