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NEW QUESTION: 1
A company has decided to update their usage policy to allow employees to surf the web unrestricted from their work computers. Which of the following actions should the IT security team implement to help protect the network from attack as a result of this new policy?
A. Install host-based anti-malware software
B. Add an implicit deny to the core router ACL
C. Implement MAC filtering on all wireless access points
D. Require users to utilize two-factor authentication
E. Block port 80 outbound on the company firewall
Answer: A
Explanation:
Explanation/Reference:
Explanation:
To protect the computers from employees installing malicious software they download on the internet, antimalware should be run on all systems.
After a single machine in a company is compromised and is running malicious software (malware), the attacker can then use that single computer to proceed further into the internal network using the compromised host as a pivot point. The malware may have been implemented by an outside attacker or by an inside disgruntled employee.
NEW QUESTION: 2
Mark works as a System Administrator for uCertify Inc. He is responsible for securing the network of the organization. He is configuring some of the advanced features of the Windows firewall so that he can block the client machine from responding to pings. Which of the following advanced setting types should Mark change for accomplishing the task?
A. UDP
B. SMTP
C. SNMP
D. ICMP
Answer: D
NEW QUESTION: 3
Joshua would like to have a cookie set on his Movie Rental XPage before it loads on the browser. Which view would Joshua use to add this functionality?
A. Controls view
B. Properties view
C. Events view
D. Applications view
Answer: C
NEW QUESTION: 4
The difference between true severity and the best approximation of the true severity is called:
A. Fitting error
B. Estimation error
C. Total error
D. Approximation error
Answer: D
Explanation:
Explanation
This question relates to fitting a distribution to the true severity of the operational risk loss we are trying to model. The quality of the fit, or the precision of the fit, has two elements to the difference between the severity as represented by our model and the true severity. To understand this, consider the three data points below:
a. The true severity,
b. The best approximation of the true severity in the model space, and
c. The fit based on the dataset.
- True severity is what we are trying to model.
- The model space refers to the collection of analytical distributions (log-normal, burr etc) that we are considering to arrive at the estimate of the severity.
- The 'best approximation of the true severity in the model space' is reached by estimating the parameters of the distribution that optimizes the risk functional.
- The 'fit' is the actual parameter estimates we settle for with the distribution we have determined best fits the true estimate of our severity. When estimating parameters, we have various methods available for estimation - the least squares method, the maximum likelihood method, for example, and we can get different estimates depending upon the method we choose to use.
Our severity model will be different from the true severity, and the total difference can be split into two types of errors:
1. Fitting error, represented by 'c - b' above: The difference between the fit based on the dataset and the best approximation of the true severity is called 'fitting error', ie, a measure of the extent to which we could have estimated the parameters better.
2. Approximation error, represented by 'b - a' above: Approximation error is the difference between the true severity, and the best approximation of the true severity that can be achieved within the model space is called
'approximation error'.
One can reduce the approximation error by expanding the model space by adding more distributions. This will reduce the approximation error, but generally has the effect of increasing the fitting error because the complexity of the model space increases, and there are more ways to fit to the true severity.