Answer:
The five factors to consider when trying to choose between a Solid State Drive, a Hard Disk Drive and, an External Hard Disk Drive are:
- Read/Write Speed
- Weight
- Power Consumption
- Cost
- Storage Capacity
- Solid State Drives (SSDs) are typically lighter in weight, faster and do not consume much power.
- Hard Disk Drives are relatively cheaper than SSDs. They also come with higher storage capacities but are more power-hungry and slower because they rely on mechanical/moving parts to read and write data.
- External HDDs are the cheapest of the three. They are not internal which is a major drawback given the additional weight. However, they come with gargantuan storage capacities that make you want to rethink having one. Besides, unlike SSDs, you can easily get them in computer accessories shops offline or online.
Cheers!
From server do some file transfer to client PC to sync to the server.
<u>Explanation:</u>
As IT System administrator if PC or client or workstation or laptop not connected to network more than 3 months from windows server we need to refresh the connection and redo the connection from server to the client to do sync the activities.
Moreover from domain server refresh and re sync activities to establishing the connection.
Go to client PC or workstation or desktop login log and logout from the PC and login to domain account by changing the password.
Answer:
It goes like:
public class Program
{
public static void main(String[] args)
{
int j=18;
int sum=0;
for (int i =1; i<7; i++)
{
sum=sum+(i*(j-2));
j=j-2;
}
System.out.println(sum);
}
}
Explanation:
<u>Variables used: </u>
j : controls the first number in product and decreases by 2 each time the loop runs.
sum: saves the values of addition as the loop runs.
Answer:
Chech the explanation
Explanation:
<em>In [16]:</em>
<em />
# Your answer to this question might be written on more than a line.
datascience_trials = make_array()
for i in np.arange(1000):
datascience_trials = np.append(datascience_trials, simulate_several_key_strikes(1))
datascience_proportion = np.count_nonzero(datascience_trials == 'datascience')/1000
datascience_proportion
<em>Out [16]:</em>
0.0
<em>In [17]:</em>
_ = ok.grade('q2_4')
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#Running tests
