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NEW QUESTION: 1
AWS 지원과 관련하여 고객이 티켓을 즉시 열 수 있도록 허용하지 않고 EC2 인스턴스에 20 분 동안 연결할 수 없는 이유는 무엇입니까?
A. 여기에 나열된 모든 이유 때문입니다.
B. AWS가 정기 유지 관리를 수행하는 경우 매일 20 분 동안 모든 EC2 인스턴스에 연결할 수 없기 때문입니다.
C. 처음 시작할 때 모든 EC2 인스턴스에 20 분 동안 연결할 수 없기 때문입니다.
D. 대부분의 접근성 문제는 자동화 된 프로세스로 20 분 이내에 해결되기 때문입니다.
Answer: D
Explanation:
An EC2 instance must be unreachable for 20 minutes before opening a ticket, because most reachability issues are resolved by automated processes in less than 20 minutes and will not require any action on the part of the customer. If the instance is still unreachable after this time frame has passed, then you should open a case with support.
Reference: https://aws.amazon.com/premiumsupport/faqs/

NEW QUESTION: 2
Refer to the exhibit.

A network engineer wants to analyze all incoming and outgoing packets for an interface that is connected to an access switch. Which three items must be configured to mirror traffic to a packet sniffer that is connected to the distribution switch? (Choose three.)
A. A monitor session on the distribution switch with a physical interface as the source and a physical interface as the destination
B. A remote SPAN VLAN on the distribution and access layer switch
C. A monitor session on the distribution switch with a physical interface as the source and the remote SPAN VLAN as the destination
D. A monitor session on the distribution switch with a remote SPAN VLAN as the source and physical interface as the destination
E. A monitor session on the access switch with a physical interface source and the remote SPAN VLAN as the destination
F. A monitor session on the access switch with a remote SPAN VLAN source and the physical interface as the destination
Answer: B,D,E
Explanation:
Explanation/Reference:
Explanation:
You can analyze network traffic passing through ports or VLANs by using SPAN or RSPAN to send a copy of the traffic to another port on the switch or on another switch that has been connected to a network analyzer or other monitoring or security device. SPAN copies (or mirrors) traffic received or sent (or both) on source ports or source VLANs to a destination port for analysis.
RSPAN supports source ports, source VLANs, and destination ports on different switches (or different switch stacks), enabling remote monitoring of multiple switches across your network. The traffic for each RSPAN session is carried over a user-specified RSPAN VLAN that is dedicated for that RSPAN session in all participating switches. The RSPAN traffic from the source ports or VLANs is copied into the RSPAN VLAN and forwarded over trunk ports carrying the RSPAN VLAN to a destination session monitoring the RSPAN VLAN. Each RSPAN source switch must have either ports or VLANs as RSPAN sources. The destination is always a physical port.
Reference: http://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst3750x_3560x/software/release/12-
2_55_se/configuration/guide/3750xscg/swspan.html

NEW QUESTION: 3
Which of the following are valid methods for selecting an appropriate model from the model space for severity estimation:
I. Cross-validation method
II. Bootstrap method
III. Complexity penalty method
IV. Maximum likelihood estimation method
A. II and III
B. All of the above
C. I, II and III
D. I and IV
Answer: B
Explanation:
Explanation
Once we have a number of distributions in the model space, the task is to select the "best" distribution that is likely to be a good estimate of true severity. We have a number of distributions to pick from, an empirical dataset (from internal or external losses), and we can estimate the parameters for the different distributions.
We then have to decide which distribution to pick, and that generally requires considering both approximation and fitting errors.
There are three methods that are generally used for selecting a model:
1. The cross-validation method: This method divides the available data into two parts - the training set, and the validation set (the validation set is also called the 'testing set'). Parameter estimation for each distribution is done using the training set, and differences are then calculated based on the validation set. Though the temptation may be to use the entire data set to estimate the parameters, that is likely to result in what may appear to be an excellent fit to the data on which it is based, but without any validation. So we estimate the parameters based on one part of the data (the training set), and check the differences we get from the remaining data (the validation set).
2. Complexity penalty method: This is similar to the cross-validation method, but with an additional consideration of the complexity of the model. This is because more complex models are likely to produce a more exact fit than simpler models, this may be a spurious thing - and therefore a 'penalty' is added to the more complex models as to favor simplicity over complexity. The 'complexity' of a model may be measured by the number of parameters it has, for example, a log-normal distribution has only two parameters while a body-tail distribution combining two different distributions may have many more.
3. The bootstrap method: The bootstrap method estimates fitting error by drawing samples from the empirical loss dataset, or the fit already obtained, and then estimating parameters for each draw which are compared using some statistical technique. If the samples are drawn from the loss dataset, the technique is called a non-parametric bootstrap, and if the sample is drawn from an estimated model distribution, it is called a parametric bootstrap.
4. Using goodness of fit statistics: The candidate fits can be compared using MLE based on the KS distance, for example, and the best one selected. Maximum likelihood estimation is a technique that attempts to maximize the likelihood of the estimate to be as close to the true value of the parameter. It is a general purpose statistical technique that can be used for parameter estimation technique, as well as for deciding which distribution to use from the model space.
All the choices listed are the correct answer.