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NEW QUESTION: 1
You have an Azure web app named App1 that contains the following autoscale conditions:
Every autoscale condition rule is configured to have a duration of 20 minutes and a cool down time of 10 minutes.
At 06:00, WebApp1 is running four instances.
You need to identify how many instances are running on WebApp1 based on the percentage of the CPU utilization.
How many instances should you identify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: 3
At 6:00 the default 4 instances are running. The CPU utilization averages 10% for 25 minutes. The scale in rules states that 1 instance should be removed when CPU utilization averages 30% or less over a 20 minute period.
Box 2: 6
At 6:00 the default 4 instances are running. The CPU utilization averages 70% for 25 minutes. The scale out rules states that 3 instances should be added when CPU utilization averages 70% or more over a 20 minute period. However, the maximum number of instances is set at 6.
References:
https://docs.microsoft.com/en-us/azure/architecture/best-practices/auto-scaling
https://docs.microsoft.com/en-us/azure/azure-monitor/platform/autoscale-understanding-settings
NEW QUESTION: 2
Which action is possible when a signature is triggered on the Cisco IOS IPS?
A. Deny all packets with the same source address
B. Send an email via SMTP to the administrator
Deny all packets with the same port destination
Send an SNMP alert to a monitoring system
Answer: A
NEW QUESTION: 3
A. Option A
B. Option B
C. Option C
D. Option D
Answer: C
NEW QUESTION: 4
A bank has two drive-in lanes to serve customers:
The process of making sure that the model measures what it is supposed to measure is called:
A. Validation.
B. Statistical inference.
C. Hypothesis testing.
D. Confidence coefficient analysis.
Answer: A
Explanation:
Validation is a step in the simulation procedure. Some assurance is needed that the results of the experiment will be realistic. This assurance requires validation of the model --often using historical data. If the model gives results equivalent to what actually happened, the model is historically valid. There is still some risk, however, that changes could make the model invalid for the future.