Answer:
True
Explanation:
The word processor used to be the only office machine in the year 1960s, that combined the keyboard text-entry and various printing functions of an electric typewriter with the recording memory. And this recording unit was a tape or a floppy disk, with the simplest of processor applied for text editing. Hence, the above statement that the early word processors ran on the devices that look like digital is true.
Answer:
"A set is an unordered collection. A dictionary is an unordered collection of data that stores data in key-value pairs."
Explanation:
Set =>
Collection of non-repetitive elements.
Unordered
Unindexed
No way to change items.
Dictionary =>
Collection of key-value pairs.
Unordered
Indexed
Mutable
Answer: The user had a mandatory profile.
Explanation:
A user profile is considered mandatory and such profile is known as pre-configured roaming user profile that only administrators can use to make precise settings for clients. In this type of profile, one can adjust his or her desktop settings, the adjustment are temporarily stored after logging off from the profile.
CISO: This person reports directly to the CIO and is responsible for assessing, managing, and implementing security.
Security Technician: Entry-level position and provides technical support to conFgure security hardware, implement security software, and diagnose and troubleshoot <span>problems</span>
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:
