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
Answer:
The answer is B: Differences in operating systems between the NSM server and other servers
Explanation:
All the evils on your network can be conquered effectively with the proper use NSM. NSM data equips CIRTs to detect and respond to cyber- attacks. With NSM, security analysts are able to discover intrusions early on in the process. Having highlighted all these positives, NSM encounters difficulties when faced with all of the above options apart from B.
Answer:
You will use Single Mode Fiber (SMF).
Explanation:
SMF is the best option in this case since we will be installing segments of up to 4000m. SMF is known for its capability to maintain a high-quality signal and practically zero degradation across long distances.
Answer:
My two arguments against the proposal are based on physical side effects and background noise interference.
Explanation:
To start with physical side effects, the use of speech recognition technology might make users experience physical discomfort. This is due to the fact that users who interact with the system by speaking for a long period would experience dry mouth, temporary loss of voice, vocal problems and muscle fatigue. In addition, the fact that users would always speak in an unnatural way to make the system interpret commands effectively could lead to voice strain.
Also, users need to be in a quiet environment before they can get the best out of speech recognition technology. This is because background noise can interfere with commands and create a mix-up which the system cannot interpret. In other words, when an environment is noisy, speech recognition technology would find it difficult to differentiate between users voice and background noise.
