A DevOps Engineer is working with an application deployed to 12 Amazon EC2 instances across 3 Availability Zones. New instances can be started from an AMI image. On a typical day, each EC2 instance has
30% utilization during business hours and 10% utilization after business hours. The CPU utilization has an immediate spike in the first few minutes of business hours. Other increases in CPU utilization rise gradually.
The Engineer has been asked to reduce costs while retaining the same or higher reliability.
Which solution meets these requirements?

• A.Create an Amazon EC2 Auto Scaling group using the AMI image, with a scaling action based on the Auto Scaling group's CPU Utilization average with a target of 75%. Create a scheduled action for the group to adjust the minimum number of instances to three after business hours end and reset to six before business hours begin.
• B.Create two Amazon CloudWatch Events rules with schedules before and after business hours begin and end. Create two AWS Lambda functions, one invoked by each rule. The first function should stop nine instances after business hours end, the second function should restart the nine instances before the business day begins.
• C.Create an EC2 Auto Scaling group using the AMI image, with a scaling action based on the Auto Scaling group's CPU Utilization average with a target of 75%. Create a scheduled action to terminate nine instances each evening after the close of business.
• D.Create two Amazon CloudWatch Events rules with schedules before and after business hours begin and end. Create an AWS CloudFormation stack, which creates an EC2 Auto Scaling group, with a parameter for the number of instances. Invoke the stack from each rule, passing a parameter value of three in the morning, and six in the evening.

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Your IT company is currently hosting a production environment in Elastic beanstalk. You understand that the Elastic beanstalk service provides a facility known as Managed updates which are minor and patch version updates which are periodically required for your system. Your IT supervisor is worried about the impact that these updates would have on the system. What can you tell about the Elastic beanstalk service with regards to managed updates

• A.Package updates can be configurable weekly maintenance window
• B.Elastic Beanstalk applies managed updates with no downtime
• C.Elastic Beanstalk applies managed updates with no reduction in capacity
• D.All of the above

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You are planning on using the Amazon RDS facility for Fault tolerance for your application. How does
Amazon RDSmuIti Availability Zone model work

• A.A second, standby database is deployed and maintained in a different availability zone from master,
  using synchronous replication.
• B.A second, standby database is deployed and maintained in a different availability zone from master
  using asynchronous replication.
• C.A second, standby database is deployed and maintained in a different region from master using
  asynchronous replication.
• D.A second, standby database is deployed and maintained in a different region from master using
  synchronous replication.

Correct Answer: A

Explanation
Amazon RDS Multi-AZ deployments provide enhanced availability and durability for Database (DB)
Instances, making them a natural fit for production database
workloads. When you provision a Multi-AZ DB Instance, Amazon RDS automatically creates a primary DB
Instance and synchronously replicates the data to a
standby instance in a different Availability Zone (AZ). Cach AZ runs on its own physically distinct,
independent infrastructure, and is engineered to be highly reliable.
In case of an infrastructure failure, Amazon RDS performs an automatic failover to the standby (or to a read
replica in the case of Amazon Aurora), so that you can
resume database operations as soon as the failover is complete.
The below diagram from the AWS documentation shows how this is configured

Option B is invalid because the replication is synchronous.
Option C and D are invalid because this is built around AZ and not regions.
For more information on Multi-AZ RDS, please visit the below URL:
* https://aws.amazon.com/rds/details/multi-az/

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A company is creating a software solution that executes a specific parallel-processing mechanism. The software can scale to tens of servers in some special scenarios. This solution uses a proprietary library that is license-based, requiring that each individual server have a single, dedicated license installed. The company has
200 licenses and is planning to run 200 server nodes concurrently at most.
The company has requested the following features:
* A mechanism to automate the use of the licenses at scale.
* Creation of a dashboard to use in the future to verify which licenses are available at any moment.
What is the MOST effective way to accomplish these requirements'?

• A.Upload the licenses to an Amazon DynamoDB table. Create an AWS CLI script to launch the servers by using the parameter --count, with min:max instances to launch. In the user data script, acquire an available license from the DynamoDB table. Monitor each instance and, in case of failure, replace the instance, then manually update the DynamoDB table.
• B.Upload the licenses to a private Amazon S3 bucket. Populate an Amazon SQS queue with the list of licenses stored in S3. Create an AWS CloudFormation template that uses an Auto Scaling group to launch the servers. In the user data script acquire an available license from SQS. Create an Auto Scaling lifecycle hook, then use it to put the license back in SQS after the instance is terminated.
• C.Upload the licenses to a private Amazon S3 bucket. Create an AWS CloudFormation template with a Mappings section for the licenses. In the template, create an Auto Scaling group to launch the servers. In the user data script, acquire an available license from the Mappings section. Create an Auto Scaling lifecycle hook, then use it to update the mapping after the instance is terminated.
• D.Upload the licenses to an Amazon DynamoDB table. Create an AWS CloudFormation template that uses an Auto Scaling group to launch the servers. In the user data script, acquire an available license from the DynamoDB table. Create an Auto Scaling litecycle hook, then use it to update the mapping after the instance is terminated.

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You need to migrate 10 million records in one hour into DynamoDB. All records are 1.5KB in size. The data is evenly distributed across the partition key.
How many write capacity units should you provision during this batch load?

• A.6667
• B.4166
• C.5556
• D.2778

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