AWS Certified Associate SOA-C02 Updated Exam

To meet the requirements of the workload, a company should store the data in an Amazon S3 Glacier vault and configure a vault lock policy for write-once, read-many (WORM) access. This will ensure that the data is stored securely and cannot be edited in the future. The other solutions (storing the data in an Amazon Elastic Block Store (Amazon EBS) volume and configuring AWS Key Management Service (AWS KMS) encryption, storing the data in Amazon S3 Standard-Infrequent Access (S3 Standard-IA) and configuring server-side encryption, or storing the data in Amazon S3 Standard-Infrequent Access (S3 Standard-IA) and configuring multi-factor authentication (MFA)) will not meet the requirements, as they do not provide a way to protect the audit logs from future edits.

https://docs.aws.amazon.com/zh_tw/AmazonS3/latest/userguide/object-lock.html

To analyze CloudWatch logs across multiple AWS Lambda functions for historical errors, the most operationally efficient way is to use AWS Glue and Amazon Athena.

AWS Glue and Amazon Athena:

AWS Glue can crawl the data in S3, creating a catalog that makes it queryable.

Amazon Athena allows you to run SQL queries on the data cataloged by AWS Glue.

Steps to Implement:

Set up an AWS Glue crawler to index the logs stored in S3.

Configure the crawler to create a table in the AWS Glue Data Catalog.

Use Amazon Athena to query the table for errors using SQL. Since errors have a common string prefix, you can use SQL queries to filter and find these errors.

References:

AWS Glue

Amazon Athena

# Enable instance scale-in protection for specific instance.

aws autoscaling set-instance-protection --instance-ids i-5f2e8a0d --auto-scaling-group-name my-asg --protected-from-scale-in

# Disable instance scale-in protection for the specified instance.

aws autoscaling set-instance-protection --instance-ids i-5f2e8a0d --auto-scaling-group-name my-asg --no-protected-from-scale-in

https://docs.aws.amazon.com/autoscaling/ec2/userguide/ec2-auto-scaling-instance-protection.html

To ensure that EC2 instances in an Auto Scaling group are not interrupted during message processing, the most effective method is to implement scale-in protection for the instances while they are actively processing messages. This can be done programmatically by modifying the Auto Scaling group's settings using the Amazon EC2 Auto Scaling API.

Starting Message Processing: When an instance begins processing a message, your application should make an API call to enable scale-in protection. This is done using the SetInstanceProtection action, setting the ProtectedFromScaleIn parameter to true for that specific instance.

Completing Message Processing: Once the message has been processed, another API call should be made to disable scale-in protection. This is done by calling the SetInstanceProtection action again, but this time setting the ProtectedFromScaleIn parameter to false.

This method ensures that while messages are being processed, the instances are not terminated by the Auto Scaling group regardless of any scale-in activities that might be triggered by other parameters like CPU utilization or a decrease in the number of messages in the queue.

AWS Documentation Reference:You can refer to the AWS documentation on managing instance scale-in protection in Auto Scaling groups for more details: Instance Scale-In Protection.

To configure Route 53 for high availability with failover between a primary and a secondary server, the following steps should be taken:

Create Health Checks:

Create HTTP health checks for both the primary and secondary servers. Ensure these health checks are configured to look for HTTP 2xx or 3xx status codes.