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NEW QUESTION: 1
An LTM Specialist has noticed in the audit log that there are numerous attempts to loginto the Admin account.
Theses attempts are sourced from a suspicious IP address range to the Configuration Utility of the LTM device.
How should the LTM Specialist block these attempts?
A. add the suspicious source IP addresses to the httpd deny list via tmsh
B. add the suspicious source IP addresses to the httpd deny list via Configuration Utility
C. add the permitted source IP addresses to the httpd allow list viatmsh
D. add the permitted source IP addresses to the allow list viaConfiguration Utility
Answer: C

NEW QUESTION: 2
Which of the following steps are required for computing the total loss distribution for a bank for operational risk once individual UoM level loss distributions have been computed from the underlhying frequency and severity curves:
I. Simulate number of losses based on the frequency distribution
II. Simulate the dollar value of the losses from the severity distribution III. Simulate random number from the copula used to model dependence between the UoMs IV. Compute dependent losses from aggregate distribution curves
A. III and IV
B. None of the above
C. I and II
D. All of the above
Answer: C
Explanation:
Explanation
A recap would be in order here: calculating operational risk capital is a multi-step process.
First, we fit curves to estimate the parameters to our chosen distribution types for frequency (eg, Poisson), and severity (eg, lognormal). Note that these curves are fitted at the UoM level - which is the lowest level of granularity at which modeling is carried out. Since there are many UoMs, there are are many frequency and severity distributions. However what we are interested in is the loss distribution for the entire bank from which the 99.9th percentile loss can be calculated. From the multiple frequency and severity distributions we have calculated, this becomes a two step process:
- Step 1: Calculate the aggregate loss distribution for each UoM. Each loss distribution is based upon and underlying frequency and severity distribution.
- Step 2: Combine the multiple loss distributions after considering the dependence between the different UoMs. The 'dependence' recognizes that the various UoMs are not completely independent, ie the loss distributions are not additive, and that there is a sort of diversification benefit in the sense that not all types of losses can occur at once and the joint probabilities of the different losses make the sum less than the sum of the parts.
Step 1 requires simulating a number, say n, of the number of losses that occur in a given year from a frequency distribution. Then n losses are picked from the severity distribution, and the total loss for the year is a summation of these losses. This becomes one data point. This process of simulating the number of losses and then identifying that number of losses is carried out a large number of times to get the aggregate loss distribution for a UoM.
Step 2 requires taking the different loss distributions from Step 1 and combining them considering the dependence between the events. The correlations between the losses are described by a 'copula', and combined together mathematically to get a single loss distribution for the entire bank. This allows the 99.9th percentile loss to be calculated.

NEW QUESTION: 3
What are examples of information that the Topology Service returns for an OpenFlow switch port? (Select two.)
A. Whether the port can participate in forwarding mode in a looped topology
B. Whether the port has BDDP enabled
C. Whether the port can be used as an observation point
D. Whether the port has LLDP medium endpoint discovery enabled
E. Whether the port is a connection point
Answer: A,E

NEW QUESTION: 4
You have an application running on Amazon EC2 in an Auto Scaling group. Instances are being bootstrapped dynamically, and the bootstrapping takes over 15 minutes to complete.
You find that instances are reported by Auto Scaling as being In Service before bootstrapping has completed.
You are receiving application alarms related to new instances before they have completed bootstrapping, which is causing confusion.
You find the cause: your application monitoring tool is polling the Auto Scaling Service API for instances that are In Service, and creating alarms for new previously unknown instances.
Which of the following will ensure that new instances are not added to your application monitoring tool before bootstrapping is completed?
A. Tag all instances on launch to identify that they are in a pending state.
Change your application monitoring tool to look for this tag before adding new instances, and the use the Amazon API to set the instance state to 'pending' until bootstrapping is complete.
B. Use the default Amazon CloudWatch application metrics to monitor your application's health.
Configure an Amazon SNS topic to send these CloudWatch alarms to the correct recipients.
C. Create an Auto Scaling group lifecycle hook to hold the instance in a pending: wait state until your bootstrapping is complete.
Once bootstrapping is complete, notify Auto Scaling to complete the lifecycle hook and move the instance into a pending: complete state.
D. Increase the desired number of instances in your Auto Scaling group configuration to reduce the time it takes to bootstrap future instances.
Answer: C