AWS Certified Associate SOA-C02 Syllabus Exam Questions Answers

To manage incomplete multipart uploads in an Amazon S3 bucket, creating an S3 Lifecycle rule to specifically address these uploads is the most effective method. The rule can be configured to automatically delete expired multipart upload parts, which helps in cleaning up unused data and reducing storage costs. Option A is correct as it directly addresses the requirement to manage incomplete uploads effectively. Reference on setting up S3 Lifecycle policies can be found here Amazon S3 Lifecycle.

To ensure that the Auto Scaling group scales out when CPU utilization rises above 50%, the administrator should configure the Auto Scaling group to dynamically scale based on demand. This is achieved by setting up a scaling policy that triggers based on specific CloudWatch alarms—like CPU utilization exceeding 50%. This dynamic scaling method directly responds to changes in workload, ensuring that resources are allocated efficiently and promptly as demand increases. Option C is the correct answer, aligning with best practices for managing EC2 Auto Scaling based on real-time metrics. Further guidance is available in AWS documentation on dynamic scaling Dynamic Scaling for EC2.

To record all S3 API activity, the SysOps administrator should create an AWS CloudTrail trail to log data events for all S3 objects. This solution provides comprehensive logging of all API activities at the object level within S3 buckets.

AWS CloudTrail:

CloudTrail allows you to log, continuously monitor, and retain account activity related to actions across your AWS infrastructure.

It records all API calls for Amazon S3 objects, providing a detailed history of changes made to S3 resources.

Configuration Steps:

Go to the CloudTrail console and create a new trail.

Enable data events and specify the S3 buckets to monitor.

CloudTrail will then record S3 object-level API operations such as GetObject, DeleteObject, and PutObject.

Step-by-Step Explanation:

Understand the Problem:

Each Lambda function generates 1 GB of log data daily in its own CloudWatch Logs log group.

The security team needs a count of application errors, grouped by type, across all log groups.

Analyze the Requirements:

Aggregate and analyze log data across multiple log groups.

Count and group errors by type.

Evaluate the Options:

Option A: Perform a CloudWatch Logs Insights query.

CloudWatch Logs Insights allows querying and analyzing log data.

The stats command and count function can aggregate and count errors across log groups.

Option B: Perform a CloudWatch Logs search with groupby and count.

CloudWatch Logs search does not support these functions; Logs Insights is needed for advanced queries.

Option C: Perform an Amazon Athena query.

Athena can query data in S3 but is not directly applicable to CloudWatch Logs.

Option D: Perform an Amazon RDS query.

RDS queries are for database data, not applicable to log data in CloudWatch.

Select the Best Solution:

Option A: CloudWatch Logs Insights is designed for querying and analyzing log data, making it the appropriate choice for counting and grouping errors.

References:

Amazon CloudWatch Logs Insights

CloudWatch Logs Insights provides powerful querying capabilities to aggregate and analyze log data, including counting and grouping errors.