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NEW QUESTION: 1
You have an Azure web app named App1 that contains the following autoscale conditions. The default auto created scale condition has a scale mode that has Scale to a specific instance count set to 2.
Scale condition 1 has the following configurations:
* Scale mode: Scale to a specific instance count
* Instance count 3
* Schedule: Specify start/end dates
* Start date: August 1. 2019.06:00
* End date: September 1.2019, 18:00
Scale condition 2 has the following configurations:
* Scale mode: Scale to a specific instance count
* Instance count 4
* Schedule: Repeat specific days
* Repeat every: Monday
* Start time: 06:00
* End time: 18:00
Scale condition 3 has the following configurations.

Answer:
Explanation:

Explanation:
Box 1: 5
Scale condition 1, Scale condition 2, and Scale condition 3 applies.
Scale condition 3 takes precedence as it the largest increase in the number of instances.
Box 2: 5
Scale condition 1 does not apply as its end date is exceeded.
Scale condition 2 and Scale condition 3 applies.
Scale condition 3 takes precedence as it the largest increase in the number of instances.
When you configure multiple policies and rules, they could conflict with each other. Autoscale uses the following conflict resolution rules to ensure that there is always a sufficient number of instances running:
* Scale-out operations always take precedence over scale-in operations.
* When scale-out operations conflict, the rule that initiates the largest increase in the number of instances takes precedence.
* When scale in operations conflict, the rule that initiates the smallest decrease in the number of instances takes precedence.
References:
https://docs.microsoft.com/en-us/azure/architecture/best-practices/auto-scaling

NEW QUESTION: 2

A. Option C
B. Option A
C. Option D
D. Option B
Answer: A,D

NEW QUESTION: 3
An application is being deployed with two Amazon EC2 Auto Scaling groups, each configured with an Application Load Balancer. The application is deployed to one of the Auto Scaling groups and an Amazon Route 53 alias record is pointed to the Application Load Balancer of the last deployed Auto Scaling group. Deployments alternate between the two Auto Scaling groups. Home security devices are making requests into the application. The Development team notes that new requests are coming into the old stack days after the deployment. The issue is caused by devices that are not observing the Time to Live (TTL) setting on the Amazon Route 53 alias record. What steps should the DevOps Engineer take to address the issue with requests coming to the old stacks, while creating minimal additional resources?
A. Create an Amazon CloudFront distribution. Set the two existing Application Load Balancers as origins on the distribution. After a deployment, update the CloudFront distribution behavior to send requests to the newly deployed Auto Scaling group.
B. Move the application to an AWS Elastic Beanstalk application with two environments. Perform new deployments on the non-live environment. After a deployment, perform an Elastic Beanstalk CNAME swap to make the newly deployed environment the live environment.
C. Reduce the application to one Application Load Balancer. Create two target groups named Blue and Green. Create a rule on the Application Load Balancer pointed to a single target group. Add logic to the deployment to update the Application Load Balancer rule to the target group of the newly deployed Auto Scaling group.
D. Create a fleet of Amazon EC2 instances running HAProxy behind an Application Load Balancer. The HAProxy instances will proxy the requests to one of the existing Auto Scaling groups. After a deployment the HAProxy instances are updated to send requests to the newly deployed Auto Scaling group.
Answer: C