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Amazon SOA-C03 시험요강:

주제	소개
주제 1	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 2	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 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.
주제 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.
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 (Q240-Q245):

질문 # 240
A company runs a web application on Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer (ALB).
A CloudOps engineer must implement deployments without any service interruptions. The company needs the ability to shift traffic between application versions and quickly revert to the previous version by redirecting traffic if issues occur.
Which deployment approach will meet these requirements?

A. Use AWS CodeDeploy blue/green deployment with two ALB target groups. Deploy the new version to a separate fleet. Shift traffic gradually to the new target group. Maintain the original fleet for immediate rollback if necessary.
B. Create a new AMI by using the updated application. Modify the Auto Scaling launch template.
Temporarily increase desired capacity to add new instances. Then terminate old instances while using ALB connection draining to manage the transition.
C. Implement an in-place rolling update by using Auto Scaling instance refresh on the existing target group. Configure connection draining for each instance. Update the launch template. Rely on health checks during the replacement process.
D. Design an AWS Step Functions workflow that creates a parallel Auto Scaling group and updates monitoring configurations. Configure the workflow to change instance registrations on the ALB and automatically terminate the previous fleet after a verification period.

정답：A

설명：
AWS CodeDeploy blue/green deployment with separate ALB target groups supports deploying the new application version to a separate fleet while the original fleet continues serving traffic.
Traffic can be shifted gradually to the new version, and rollback can be performed quickly by redirecting traffic back to the original target group.

질문 # 241
A medical research company uses an Amazon Bedrock powered AI assistant with agents and knowledge bases to provide physicians quick access to medical study protocols. The company needs to generate audit reports that contain user identities, usage data for Bedrock agents, access data for knowledge bases, and interaction parameters.
Which solution will meet these requirements?

A. Use Amazon CloudWatch to capture generative AI application logs. Stream the logs to Amazon OpenSearch Service. Use an OpenSearch dashboard visualization to generate reports.
B. Use AWS CloudTrail to capture generative AI application logs. Stream the logs to Amazon Managed Service for Apache Flink. Use SQL queries to generate reports.
C. Use AWS CloudTrail to log API events from generative AI workloads. Store the events in CloudTrail Lake. Use SQL-like queries to generate reports.
D. Use Amazon CloudWatch to log API events from generative AI workloads. Send the events to an Amazon S3 bucket. Use Amazon Athena queries to generate reports.

정답：C

설명：
As per AWS Cloud Operations, Bedrock, and Governance documentation, AWS CloudTrail is the authoritative service for capturing API activity and audit trails across AWS accounts. For Amazon Bedrock, CloudTrail records all user-initiated API calls, including interactions with agents, knowledge bases, and generative AI model parameters.
Using CloudTrail Lake, organizations can store, query, and analyze CloudTrail events directly without needing to export data. CloudTrail Lake supports SQL-like queries for generating audit and compliance reports, enabling the company to retrieve information such as user identity, API usage, timestamp, model or agent ID, and invocation parameters.
In contrast, CloudWatch focuses on operational metrics and log streaming, not API-level identity data.
OpenSearch or Flink would add unnecessary complexity and cost for this use case.
Thus, the AWS-recommended CloudOps best practice is to leverage CloudTrail with CloudTrail Lake to maintain auditable, queryable API activity for Bedrock workloads, fulfilling governance and compliance requirements.
Reference: AWS Cloud Operations & Governance Guide - Section: Auditing and Governance for Generative AI Workloads Using AWS CloudTrail and CloudTrail Lake

질문 # 242
A company hosts a database on an Amazon RDS Multi-AZ DB instance. The database is not encrypted. The company's new security policy requires all AWS resources to be encrypted at rest and in transit.
What should a CloudOps engineer do to encrypt the database?

A. Encrypt the standby replica in a secondary Availability Zone. Promote the standby replica to the primary DB instance.
B. Take a snapshot of the DB instance. Encrypt the snapshot. Restore the snapshot to the same DB instance.
C. Configure encryption on the existing DB instance.
D. Take a snapshot of the DB instance. Copy and encrypt the snapshot. Create a new DB instance by restoring the encrypted copy.

정답：D

설명：
Amazon RDS encryption at rest cannot be enabled directly on an existing unencrypted DB instance. AWS guidance states that encryption for an RDS DB instance is enabled when the DB instance is created, not after creation. To convert an unencrypted RDS database to encrypted, the CloudOps engineer must take a snapshot, copy the snapshot while enabling encryption with an AWS KMS key, and then restore a new DB instance from the encrypted snapshot. Restoring from a snapshot creates a new DB instance; it does not overwrite the existing DB instance. Option C is invalid because Multi-AZ standby replicas cannot be independently encrypted and promoted for this purpose. Therefore, creating an encrypted snapshot copy and restoring it as a new DB instance is correct.

질문 # 243
A company has a multi-account AWS environment that includes the following:
- A central identity account that contains all IAM users and groups
- Several member accounts that contain IAM roles
A SysOps administrator must grant permissions for a particular IAM group to assume a role in one of the member accounts. How should the SysOps administrator accomplish this task?

A. In the member account, add the group Amazon Resource Name (ARN) to the role's trust policy.
In the identity account, add an inline policy to the group with sts:AssumeRole permissions.
B. In the member account, add the group Amazon Resource Name (ARN) to the role's inline policy.In the identity account, add a trust policy to the group with sts:AssumeRole permissions.
C. In the member account, add sts:AssumeRole permissions to the role's policy. In the identity account, add a trust policy to the group that specifies the account number of the member account.
D. In the member account, add the group Amazon Resource Name (ARN) to the role's trust policy.
In the identity account, add an inline policy to the group with sts:PassRole permissions.

정답：A

설명：
Cross-account role assumption requires two explicit permissions. AWS CloudOps documentation states that the target role must trust the principal, and the principal must be allowed to call sts:AssumeRole.
In the member account, the role's trust policy must list the IAM group ARN (or the identity account) as a trusted principal. In the identity account, the IAM group must have an inline or attached policy that allows the sts:AssumeRole action for the target role ARN. This dual configuration enables secure and controlled cross-account access.

질문 # 244
A web application runs on Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer (ALB). During rolling updates, application initialization and cold starts cause 30- second latency spikes. A CloudOps engineer needs to keep latency flat by pre-initializing capacity. The CloudOps engineer must reuse instances across waves without extending the maintenance window. Which solution will meet these requirements?

A. Place a Network Load Balancer in front of the ALB to absorb burst connections. Keep the Auto Scaling group maximum unchanged. Configure zero-surge rolling replacements. Disable instance reuse.
B. Configure an Auto Scaling group warm pool with stopped instances that are pre-initialized through user data. Enable instance reuse. Set a target tracking policy with an instance warmup value.
Run instance refresh in batches so the replacements pull from the warm pool.
C. Enable predictive scaling with a 7-day forecast on the Auto Scaling group. Overlay a CPU target- tracking policy with a 300-second instance warmup. Add scheduled actions for the rollout window.Maintain zero-surge instance refresh with standard ALB health checks.
D. Double the health-check grace period. Disable connection draining. Use step scaling to absorb spikes during each wave. Move to more and smaller instances.

정답：B

설명：
An Auto Scaling warm pool keeps pre-initialized instances ready before they are placed into service. This is exactly what the scenario needs because cold starts during rolling updates are causing latency spikes. By using stopped instances that have already completed initialization, replacement capacity can join the Auto Scaling group faster and with less runtime warmup impact. Instance reuse also allows instances to return to the warm pool after scale-in or refresh waves, reducing repeated initialization overhead. Target tracking with instance warmup prevents scaling decisions from being distorted while new instances stabilize. Options B, C, and D do not directly pre-initialize application capacity. Predictive scaling forecasts demand, but it does not solve cold-start latency during controlled deployment waves. Warm pools are the purpose-built CloudOps feature for this requirement.

질문 # 245
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