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Amazon SOA-C03 Exam Syllabus Topics:

Topic	Details
Topic 1	Deployment, Provisioning, and Automation: This section measures the skills of Cloud Engineers and covers provisioning and maintaining cloud resources using AWS CloudFormation, CDK, and third-party tools. It evaluates automation of deployments, remediation of resource issues, and managing infrastructure using Systems Manager and event-driven processes like Lambda or S3 notifications.
Topic 2	Security and Compliance: This section measures skills of Security Engineers and includes implementing IAM policies, roles, MFA, and access controls. It focuses on troubleshooting access issues, enforcing compliance, securing data at rest and in transit using AWS KMS and ACM, protecting secrets, and applying findings from Security Hub, GuardDuty, and Inspector.
Topic 3	Networking and Content Delivery: This section measures skills of Cloud Network Engineers and focuses on VPC configuration, subnets, routing, network ACLs, and gateways. It includes optimizing network cost and performance, configuring DNS with Route 53, using CloudFront and Global Accelerator for content delivery, and troubleshooting network and hybrid connectivity using logs and monitoring tools.
Topic 4	Reliability and Business Continuity: This section measures the skills of System Administrators and focuses on maintaining scalability, elasticity, and fault tolerance. It includes configuring load balancing, auto scaling, Multi-AZ deployments, implementing backup and restore strategies with AWS Backup and versioning, and ensuring disaster recovery to meet RTO and RPO goals.
Topic 5	Monitoring, Logging, Analysis, Remediation, and Performance Optimization: This section of the exam measures skills of CloudOps Engineers and covers implementing AWS monitoring tools such as CloudWatch, CloudTrail, and Prometheus. It evaluates configuring alarms, dashboards, and notifications, analyzing performance metrics, troubleshooting issues using EventBridge and Systems Manager, and applying strategies to optimize compute, storage, and database performance.

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Amazon AWS Certified CloudOps Engineer - Associate Sample Questions (Q241-Q246):

NEW QUESTION # 241
A company's reporting job that used to run in 15 minutes is now taking an hour to run. An application generates the reports. The application runs on Amazon EC2 instances and extracts data from an Amazon RDS for MySQL database.
A CloudOps engineer checks the Amazon CloudWatch dashboard for the RDS instance and notices that the Read IOPS metrics are high, even when the reports are not running. The CloudOps engineer needs to improve the performance and the availability of the RDS instance.
Which solution will meet these requirements?

A. Create an Amazon CloudFront distribution. Set the RDS instance as the origin. Update the reporting job to query the CloudFront distribution.
B. Configure an Amazon ElastiCache cluster in front of the RDS instance. Update the reporting job to query the ElastiCache cluster.
C. Increase the size of the RDS instance.
D. Deploy an RDS read replica. Update the reporting job to query the reader endpoint.

Answer: D

Explanation:
A read replica offloads read traffic from the primary RDS instance to a separate replica, improving both performance (by reducing read load and IOPS on the primary) and availability (the replica can also be promoted in a failure scenario). Pointing the reporting job to the reader endpoint ensures that heavy reporting reads no longer compete with the primary's transactional workload, reducing query latency and speeding up the reports.

NEW QUESTION # 242
A CloudOps engineer needs to implement a backup strategy for Amazon EC2 resources and Amazon RDS resources. The backup strategy must meet the following retention requirements:
- Daily backups: must be kept for 6 days
- Weekly backups: must be kept for 4 weeks
- Monthly backups: must be kept for 11 months
- Yearly backups: must be kept for 7 years
Which backup strategy will meet these requirements with the LEAST administrative effort?

A. Use AWS Backup to create a new backup plan for each retention requirement with a backup frequency of daily, weekly, monthly, or yearly. Set the retention period to match the requirement.
Create tags on each resource that needs to be backed up. Set up resource assignment by using the tags.
B. Use Amazon Data Lifecycle Manager to create an Amazon EBS snapshot policy. Create tags on each resource that needs to be backed up. Create multiple schedules according to the requirements within the policy. Set the appropriate frequency and retention period.
C. Use Amazon Data Lifecycle Manager to create an Amazon EBS snapshot policy. Create tags on each resource that needs to be backed up. Set up resource assignment by using the tags. Create multiple schedules according to the requirements within the policy. Set the appropriate frequency and retention period. In Amazon RDS, activate automated backups on the required DB instances.
D. Create an AWS Lambda function. Program the Lambda function to use native tooling to take backups of file systems in Amazon EC2 and to make copies of databases in Amazon RDS.
Create an Amazon EventBridge rule to invoke the Lambda function.

Answer: A

Explanation:
AWS Backup provides centralized backup management for both Amazon EC2 and Amazon RDS resources. Separate backup plans can implement the required daily, weekly, monthly, and yearly schedules with matching retention periods, while tag-based resource assignments automatically include the appropriate resources. This provides the required retention strategy with the least administrative effort.

NEW QUESTION # 243
A CloudOps engineer is setting up a fleet of Amazon EC2 instances in an Auto Scaling group for an application. The fleet should have 50% CPU available at all times to accommodate bursts of traffic. The load will increase significantly between the hours of 09:00 and 17:00, 7 days a week.
How should the CloudOps engineer configure the scaling of the EC2 instances to meet these requirements?

A. Create a target tracking scaling policy that runs when the CPU utilization is higher than 50%.
Create a scheduled scaling policy that ensures that the fleet is available at 09:00. Create a second scheduled scaling policy that scales in the fleet at 17:00.
B. Create a target tracking scaling policy that runs when the CPU utilization is higher than 90%.
C. Set the Auto Scaling group to start with 2 instances by setting the desired instances, maximum instances, and minimum instances to 2. Create a scheduled scaling policy that ensures that the fleet is available at 09:00.
D. Create a scheduled scaling policy that ensures that the fleet is available at 09:00. Create a second scheduled scaling policy that scales in the fleet at 17:00.

Answer: A

Explanation:
A target tracking scaling policy with a 50% CPU utilization target maintains roughly 50% spare CPU capacity for unexpected traffic bursts. Scheduled scaling can proactively increase capacity before the predictable 09:00-17:00 demand period and reduce capacity afterward, combining reactive scaling with preparation for known traffic patterns.

NEW QUESTION # 244
A CloudOps engineer configures an application to run on Amazon EC2 instances behind an Application Load Balancer (ALB) in a simple scaling Auto Scaling group with the default settings. The Auto Scaling group is configured to use the RequestCountPerTarget metric for scaling. The CloudOps engineer notices that the RequestCountPerTarget metric exceeded the specified limit twice in 180 seconds.
How will the number of EC2 instances in this Auto Scaling group be affected in this scenario?

A. The Auto Scaling group will launch one EC2 instance and will wait for the default cooldown period before launching another instance.
B. The Auto Scaling group will send an alert to the ALB to rebalance the traffic and not add new EC2 instances until the load is normalized.
C. The Auto Scaling group will try to distribute the traffic among all EC2 instances before launching another instance.
D. The Auto Scaling group will launch an additional EC2 instance every time the RequestCountPerTarget metric exceeds the predefined limit.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Doocuments:
With simple scaling policies, an Auto Scaling group performs one scaling activity when the alarm condition is met, then observes a default cooldown period (300 seconds) before another scaling activity of the same type can begin. CloudOps guidance explains that cooldown prevents rapid successive scale-outs by allowing time for the newly launched instance(s) to register with the load balancer and impact the metric. Even if the alarm breaches multiple times during the cooldown window, the group waits until the cooldown completes before evaluating and acting again. In this case, although RequestCountPerTarget exceeded the threshold twice within 180 seconds, the group will launch a single instance and then wait for cooldown before any additional scale-out can occur. Options A, C, and D do not reflect the behavior of simple scaling with cooldowns; A describes step/target-tracking-like behavior, and C/D are not Auto Scaling mechanics.
References (AWS CloudOps Documents / Study Guide):
* Amazon EC2 Auto Scaling - Simple Scaling Policies and Cooldown (User Guide)
* Elastic Load Balancing Metrics - ALB RequestCountPerTarget (CloudWatch Metrics)
* AWS Well-Architected Framework - Performance Efficiency & Operational Excellence

NEW QUESTION # 245
A company has multiple AWS accounts that run applications in different AWS Regions. The company has configured Amazon CloudWatch alarms in each account to monitor CPU utilization and request latency metrics.
An operations team must create a centralized dashboard in a single Region within the operations team's AWS account. The dashboard needs to be able to display metrics and alarm states from all applications.
Which solution will meet these requirements?

A. Create an Amazon EventBridge rule in each application account to forward CloudWatch alarm state changes to an Amazon SNS topic in the operations team's AWS account. In the operations team's AWS account, create a CloudWatch dashboard with the metrics and alarms from the application accounts.
B. Enable the CloudWatch cross-account cross-Region console in each application account.
Configure the sharing of metrics and alarms with the operations team's AWS account. In the operations team's AWS account, create a CloudWatch dashboard with the metrics and alarms from the application account.
C. Deploy AWS Lambda functions in each application account. Configure the functions to periodically extract CloudWatch metrics and alarm states and copy the values to CloudWatch in the operations team's AWS account. In the operations team's AWS account, create a CloudWatch dashboard with the metrics and alarms from the application accounts.
D. Set up CloudWatch cross-account observability. Designate the operations team's AWS account as the monitoring account. Configure the application accounts as source accounts. In the operations team's AWS account, create a CloudWatch dashboard with the metrics and alarms from the application accounts.

Answer: D

Explanation:
CloudWatch cross-account observability is designed for centralized monitoring across multiple AWS accounts and Regions. By configuring the operations account as the monitoring account and the application accounts as source accounts, the operations team can create dashboards that display shared metrics and alarm states without copying monitoring data between accounts.

NEW QUESTION # 246
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