Pass the Amazon Web Services AWS Certified Associate SOA-C03 Questions and answers with CertsForce

According to the AWS Cloud Operations and Elastic Load Balancing documentation, Application Load Balancer (ALB) supports multiple routing algorithms to distribute requests among targets:

Round robin (default)

Least outstanding requests (LOR)

Weighted random

When applications require session affinity, AWS recommends using “least outstanding requests” as the load balancing algorithm because it reduces latency, distributes load evenly, and ensures consistent target responsiveness during high traffic.

Using weighted random routing with sticky sessions can cause sessions to be routed inconsistently if one target’s capacity fluctuates, leading to session mismatches and application errors — especially when user sessions rely on instance-specific state.

Disabling cross-zone balancing (Option C) or adjusting deregistration delay (Option D) does not address routing inconsistency. Anomaly mitigation (Option B) protects against target performance degradation, not sticky-session misrouting.

Therefore, the correct solution is Option A — changing the target group’s routing algorithm to least outstanding requests ensures smoother, predictable session handling and resolves random application errors.

The reporting workload is read-heavy, and the database shows high Read IOPS even outside the report window, suggesting sustained read pressure from other workloads or inefficient read patterns. The requirement is to improve both performance and availability of the RDS for MySQL instance. An RDS read replica is designed specifically to offload read traffic from the primary database instance and to provide additional capacity for read-heavy use cases such as reporting, analytics queries, and dashboards.

By deploying one or more read replicas, the company can direct the reporting job to a replica (Option B). This reduces contention on the primary instance, lowers read I/O demand on the writer, and can improve overall query latency and throughput. In addition, read replicas can contribute to availability objectives: if the primary instance has issues, replicas can be promoted (manually or as part of certain DR patterns) to become a new standalone database, reducing recovery time for read availability and providing a practical resilience option.

Option A (ElastiCache) can help for highly cacheable and repetitive queries, but it requires application/query redesign and cache invalidation strategy, and it does not inherently improve database availability. Option C is not valid because CloudFront is a CDN for HTTP content and is not an appropriate layer for database queries. Option D (vertical scaling) can improve performance, but it does not offload reads and often involves higher cost; it also does not provide the same availability and read scaling benefits as replicas.

Therefore, adding an RDS read replica and pointing the reporting workload to the reader endpoint best meets the performance and availability requirements.

Per the AWS Cloud Operations and Data Protection documentation, S3 Object Lock enforces write-once-read-many (WORM) protection on objects for a defined retention period.

There are two modes:

Compliance mode: Even the root user cannot delete or modify objects during the retention period.

Governance mode: Privileged users with special permissions can override lock settings.

For regulatory or audit requirements that prohibit deletion, Compliance mode is the correct choice. When configured with a 3-month retention period, all backup objects are protected from deletion until expiration, ensuring compliance with data retention mandates.

Versioning (Option C) alone does not prevent deletion. IAM-based restrictions (Option A) lack time-based enforcement and require manual intervention. Governance mode (Option D) is less strict and unsuitable for regulatory retention.

Thus, Option B is the correct CloudOps solution for immutable S3 backups.

Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Documents:

The most operationally efficient solution is C because AWS Systems Manager Session Manager is purpose-built for secure, auditable interactive access to EC2 instances at scale—without managing bastion hosts or distributing SSH keys. Session Manager can be configured to log session activity, including commands and output, to durable destinations such as Amazon CloudWatch Logs (and optionally Amazon S3). This directly satisfies the requirement to record interactive sessions and store logs durably.

For automated notifications and alarms, CloudWatch Logs supports metric filters that transform matching log patterns into CloudWatch metrics. Those metrics can then drive CloudWatch alarms and notifications (for example, via Amazon SNS). This is a standard CloudOps pattern: centralize logs, derive metrics from security-relevant patterns, and alert automatically.

Option A and D require installing and operating agents and building a more complex analytics path (Athena queries for alerting), which is less efficient and introduces more moving parts across thousands of instances. Option B adds a bastion host dependency that becomes an operational burden (scaling, patching, hardening, HA) and a potential choke point. Session Manager reduces these burdens by using SSM Agent already installed, IAM-based access control, and centralized logging/monitoring integrations.

Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Documents:

To ensure video files are cached at CloudFront edge locations after migrating the files to Amazon S3, CloudFront must be able to fetch those video objects directly from S3 as an origin. The most operationally straightforward pattern is to add S3 as a second origin and create a separate cache behavior that routes requests for video paths (for example, /video/* or /*.mp4) to the S3 origin. With this configuration, CloudFront caches the S3-served objects at edge locations according to the cache policy/headers, while the existing ALB origin continues serving dynamic application paths. This isolates static media delivery from the application tier and improves performance by maximizing cache hits at the edge.

Option A is not how CloudFront origin selection works: CloudFront does not “redirect” to S3 based on headers; it selects an origin per behavior. Option C (signed URLs) is an access-control mechanism and does not, by itself, ensure the objects are retrieved from S3 or cached correctly. Option D (origin groups) is for origin failover (primary/secondary) and does not provide path-based routing to ensure videos come from S3.

User-defined tags must be explicitly activated as cost allocation tags in the AWS Billing and Cost Management console before they can be used in Cost Explorer. Simply applying tags to resources is not sufficient.

Once activated, cost allocation tags can take up to 24 hours to appear in Cost Explorer, but they will not appear at all if activation is not performed. The 30-day delay applies only to historical reporting after activation, not to visibility itself.

Cost and Usage Reports and Budgets are not prerequisites for Cost Explorer filtering.

Therefore, the issue occurs because the tags were not activated for cost allocation.

According to the AWS Cloud Operations and Auto Scaling documentation, scaling applications that consume Amazon SQS messages should be driven by queue backlog per instance, not by general system metrics such as network traffic or CPU.

The correct approach is to calculate a custom metric using CloudWatch metric math that divides the SQS metric ApproximateNumberOfMessagesVisible by the number of active EC2 instances in the Auto Scaling group. This “backlog per instance” value represents the average number of messages waiting to be processed by each instance.

Then, the CloudOps engineer can create a target tracking policy that automatically scales out or in based on maintaining a desired backlog threshold. This approach ensures dynamic, workload-driven scaling behavior that reacts in near real time to message volume.

Step and simple scaling (Options C and D) require manual thresholds and do not automatically balance the load per instance.

Thus, Option B—using CloudWatch metric math to define queue backlog per instance for target tracking—is the most effective and AWS-recommended CloudOps practice.

The PrivateDnsName attribute of an EC2 instance is automatically assigned by AWS at launch time and is a read-only property. CloudFormation does not allow users to specify this value manually.

Including PrivateDnsName in the EC2 instance properties causes validation to fail during stack creation. Outputs and Parameters sections are optional, and the VPC is implicitly defined through the subnet ID.

Therefore, attempting to set PrivateDnsName results in stack creation failure.

The CloudWatch agent supports modular configuration using the append-config option, which allows additional log sources to be added without overwriting the existing baseline configuration. This makes it ideal for incremental log collection changes across large fleets.

By creating a separate configuration file specifically for DHCP logs and using Systems Manager Run Command, the CloudOps engineer can remotely update only the relevant instances in a scalable and automated manner. This approach avoids manual login and preserves the existing baseline configuration.

Options B and C require manual intervention on each instance, which is not scalable or operationally efficient. Option D captures additional logs indiscriminately and does not specifically target DHCP logs, potentially increasing noise and cost.

Therefore, Option A is the most efficient and AWS-recommended approach.

The AWS Cloud Operations and Governance documentation specifies that AWS Config can be paired with AWS Systems Manager Automation runbooks for automatic remediation of noncompliant resources.

For SSH restrictions, the restricted-ssh managed rule detects any security group allowing inbound traffic on port 22. To automatically remediate these findings, AWS provides the AWS-DisableIncomingSSHOnPort22 runbook. This runbook programmatically removes inbound rules that allow port 22 traffic from affected security groups.

This approach achieves continuous compliance with minimal human intervention. By contrast, sending notifications (Option A) does not enforce remediation, API-based scripts (Option C) add operational overhead, and manual remediation (Option D) violates automation best practices.

Therefore, the most efficient CloudOps solution is Option B, using AWS Config with the AWS-DisableIncomingSSHOnPort22 automation runbook for automatic, scalable enforcement.