Answer:
69.08265412 milliseconds
Explanation:
Lets first convert 7 MiB to bits
bits
Now convert bits to Gbits
Gbits
Queuing Delay = Total size/transmission link rate
Queuing Delay= 
seconds
Delay of packet number 3 = 
seconds
or 
milliseconds
Answer:
Following are the code in the PHP Programming Language:
<u>foreach ($country_codes as $code => $name) {
</u>
Explanation:
The following option is true because the Foreach statement only works on the objects and array and this statement is good for accessing the key and value pairs from the array.
So, that's why to print following associative array through echo statement we use the Foreach loop statement with following right condition.
<u>syntax</u>:
foreach (array as value)
{
code or body to execute;
}
Answer:
Explanation:
An Access Control Matrix ACM can be defined as a table that maps the permissions of a set of subjects to act upon a set of objects within a system. The matrix is a two-dimensional table with subjects down the columns and objects across the rows. The permissions of the subject to act upon a particular object are found in the cell that maps the subject to that object.
Summary
The rows correspond to the subject
The columns correspond to the object
What does each cell in the matrix contain? Answer: Each cell is the set of access rights for that subject to that object.
Answer:
SELECT vendor_number, vendor_name, CONCAT ('street', ' ' , 'city', ' ' , 'state', ' ' , 'zip code') as adress
FROM vendor_directory
ORDER BY vendor_name ASC;
Explanation:
* Suppose <u>vendor_directory</u> is the name of the table from which you extract the data with the SELECT sentence.
Answer:
a. This is an instance of overfitting.
Explanation:
In data modeling and machine learning practice, data modeling begins with model training whereby the training data is used to train and fit a prediction model. When a trained model performs well on training data and has low accuracy on the test data, then we say say the model is overfitting. This means that the model is memorizing rather Than learning and hence, model fits the data too well, hence, making the model unable to perform well on the test or validation set. A model which underfits will fail to perform well on both the training and validation set.
