SOA-C03 Exam Dumps : AWS Certified CloudOps Engineer - Associate

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.

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The requirement is to produce cost optimization recommendations for EBS volumes based on IOPS and throughput, and to do so with the most operational efficiency across potentially many instances and volumes. AWS Compute Optimizer is the most suitable service because it analyzes historical utilization metrics and configuration characteristics to generate rightsizing recommendations. When enabled, Compute Optimizer evaluates EBS volume performance patterns (including throughput and IOPS consumption) and can recommend more appropriate volume types or settings to reduce cost while meeting performance needs.

Option C is operationally efficient because it centralizes analysis. Instead of manually inspecting per-volume graphs, Compute Optimizer aggregates and evaluates data over time, reducing human effort and improving consistency. After sufficient observation time, the CloudOps engineer can review the findings and translate them into concrete actions such as reducing provisioned IOPS on io1/io2, selecting gp3 with tuned IOPS/throughput, or resizing volumes that are over-provisioned relative to actual demand.

Option A is manual and does not directly address IOPS/throughput rightsizing; it also incorrectly focuses on consumed space versus provisioned space, which is more relevant to capacity than performance provisioning. Option B is unrelated: EBS-optimized is an EC2 feature for dedicated EBS bandwidth and does not rightsize EBS volume performance or reduce over-provisioned IOPS/throughput. Option D introduces significant operational overhead by installing benchmarking tools and running synthetic tests, which is not scalable, can affect production performance, and still may not reflect real workload patterns.

Therefore, enabling AWS Compute Optimizer and using its EBS volume recommendations is the most operationally efficient approach.

Per the AWS Cloud Operations and Service Catalog documentation, when a portfolio is shared across AWS accounts, the recipient account imports the shared portfolio.

The recipient CloudOps engineer cannot modify the original products or their configurations but can:

Add products from the imported portfolio into their local portfolios for deployment,

Control end-user access in the recipient account, and

Manage local constraints or permissions.

However, the recipient cannot edit, delete, or reconfigure the shared products (Options B, C, and D). The source (owner) account retains full administrative control over products, launch roles, and lifecycle policies.

This model aligns with AWS CloudOps principles of centralized governance with distributed self-service deployment across multiple accounts.

Thus, Option A is correct—imported portfolios allow the recipient to add products to a local portfolio but not alter the shared configuration.