SOA-C03덤프공부덤프로AWS Certified CloudOps Engineer - Associate시험도전

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Amazon SOA-C03 시험요강:

주제소개
주제 1
  • 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.
주제 2
  • 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.
주제 3
  • 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.
주제 4
  • 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.
주제 5
  • 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.

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최신 Amazon Associate SOA-C03 무료샘플문제 (Q46-Q51):

질문 # 46
A CloudOps engineer has an Amazon ECS service that runs a transaction processing application.
The CloudOps engineer needs to deploy a new feature on the ECS service. The feature cannot have downtime during deployment. The feature must also have the ability to run an immediate one-step rollback if a performance-degrading bug is detected. Which solution will meet these requirements?

정답：D

설명：
Blue/green deployment with AWS CodeDeploy is the correct strategy for Amazon ECS when zero downtime and fast rollback are required. In a blue/green deployment, the current production task set remains active while a new task set is created and validated. Traffic can then be shifted safely to the new version. If the new version causes performance degradation, CodeDeploy can roll back by redirecting traffic to the previous task set. AWS ECS documentation describes blue/green deployment as a method that reduces downtime and risk by running two production environments and validating revisions before routing production traffic. Rolling updates do not provide the same immediate one-step rollback. Canary or linear traffic shifting gradually moves traffic but does not match the requirement for immediate rollback as directly as blue/green.
Manual scaling is operationally weak.


질문 # 47
A CloudOps engineer is helping a development team deploy an application to AWS. The AWS CloudFormation template includes an Amazon Linux EC2 instance, an Amazon Aurora DB cluster, and a hardcoded database password that must be rotated every 90 days. What is the MOST secure way to manage the database password?

정답：D

설명：
The most secure solution is to remove the hardcoded password completely and let AWS Secrets Manager generate, store, and rotate the database password. The AWS::SecretsManager::Secret resource with GenerateSecretString avoids exposing a static password in templates, parameters, source control, or deployment logs. The AWS::SecretsManager::RotationSchedule resource automates the required 90-day password rotation. The application should retrieve the secret at runtime from Secrets Manager by using an IAM role with least-privilege access. Option B still accepts a human-provided password, which is weaker than automatic secret generation.
Parameter Store can store secure values, but it does not provide the same native database credential rotation workflow as Secrets Manager. NoEcho only masks console output; it does not make user data or template handling sufficiently secure.


질문 # 48
A SysOps administrator is configuring an Auto Scaling group of Amazon EC2 instances for an application. The average CPU utilization of the instances in the Auto Scaling group must remain at approximately 40% when the load on the application changes.
Which solution will meet this requirement in the MOST operationally efficient manner?

정답：D

설명：
Target tracking scaling policies are designed to automatically maintain a specific metric at a desired target value with the least administrative effort. AWS CloudOps documentation states that target tracking works like a thermostat, continuously adjusting capacity to keep the selected metric close to the defined target.
By specifying a target value of 40% average CPU utilization, the Auto Scaling group automatically scales out or in based on real-time demand without requiring manual thresholds, multiple alarms, or predefined scaling steps. This approach is fully managed by AWS and is the most operationally efficient option.


질문 # 49
A company has a workload that is sending log data to Amazon CloudWatch Logs. One of the fields includes a measure of application latency. A CloudOps engineer needs to monitor the p90 statistic of this field over time.
What should the CloudOps engineer do to meet this requirement?

정답：C

설명：
To analyze and visualize custom statistics such as the p90 latency (90th percentile), a CloudWatch metric must be generated from the log data. The correct method is to create a metric filter that extracts the latency value from each log event and publishes it as a CloudWatch metric. Once the metric is published, percentile statistics (p90, p95, etc.) can be displayed in CloudWatch dashboards or alarms.
AWS documentation states:
"You can use metric filters to extract numerical fields from log events and publish them as metrics in CloudWatch. CloudWatch supports percentile statistics such as p90 and p95 for these metrics." Contributor Insights (Option A) is for analyzing frequent contributors, not numeric distributions. Subscription filters (Option C) are used for log streaming, and Application Insights (Option D) provides monitoring of application health but not custom p90 statistics. Hence, Option B is the CloudOps-aligned, minimal-overhead solution for percentile latency monitoring.
References:* AWS Certified CloudOps Engineer - Associate (SOA-C03) Exam Guide - Domain 1:
Monitoring and Logging* Amazon CloudWatch Logs - Metric Filters* AWS Well-Architected Framework - Operational Excellence Pillar


질문 # 50
A company observes a dramatic increase in 500 status code responses from an HTTP application that runs on Amazon EC2 instances. The EC2 instances are in an Auto Scaling group and use EC2 health checks for resiliency. The company uses Amazon CloudWatch to collect logs for the EC2 instances and the HTTP server logs.
A CloudOps engineer investigates the cause of the status codes. The CloudOps engineer finds that errors correlate with times when the Auto Scaling group was either replacing EC2 instances or performing scale-in actions. The CloudOps engineer needs to improve the resiliency of the application's architecture.
Which solution will meet this requirement?

정답：C

설명：
The errors occur during Auto Scaling replacement and scale-in events, which strongly indicates that instances are being terminated or recycled while they are still serving traffic. When an Auto Scaling group uses only EC2 status checks, the health evaluation is limited to instance-level signals (such as system reachability and instance reachability). Those checks do not validate whether the application process is healthy, whether the web server is still responding correctly, or whether the instance is safely able to continue serving requests while shutdown activities are underway. As a result, traffic can continue to reach an instance that is about to be terminated, or a newly launched instance can be marked healthy at the EC2 layer before the application is actually ready, producing spikes in 5xx responses.
Using Elastic Load Balancing health checks integrates the Auto Scaling group with the load balancer's application-aware health evaluation. The load balancer can perform health checks against a specific endpoint (for example, /health) over HTTP/HTTPS and determine whether the application is responding successfully.
Auto Scaling can then replace instances based on real service health rather than only infrastructure health.
This approach improves resiliency because unhealthy or draining instances are removed from load balancing before they cause user-facing errors, and newly launched instances are kept out of rotation until they pass the ELB health checks.
Increasing cooldown (A) only slows scaling actions and does not ensure safe traffic draining. Increasing minimum capacity (C) can reduce impact but does not address the root cause of instances receiving traffic during lifecycle changes. Increasing grace period (D) helps initial warm-up, but it does not reliably protect users during scale-in and termination without application-level health integration. Therefore, ELB health checks are the best solution.


질문 # 51
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