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

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.

The CloudWatch agent procstat plugin is specifically designed to collect per-process metrics such as CPU usage by process ID. It allows continuous, automated monitoring without manual intervention and integrates directly with CloudWatch.

Default CloudWatch metrics provide only instance-level CPU utilization and do not expose process-level details. Manual logins and Lambda-based polling introduce unnecessary operational overhead and are not scalable.

Therefore, configuring the procstat plugin is the most efficient solution.

The AWS Cloud Operations and Compute documentation explains that placement groups control how EC2 instances are physically arranged within AWS data centers to optimize network performance.

Among the available placement strategies:

Cluster placement groups place instances physically close together within a single Availability Zone, connected through high-bandwidth, low-latency networking (ideal for tightly coupled, HPC, or distributed workloads).

Spread placement groups distribute instances across distinct racks or Availability Zones for fault tolerance, increasing latency.

Partition placement groups separate instances into partitions for isolation, not latency reduction.

Therefore, to minimize latency for workloads such as computational clusters, the CloudOps engineer should use a cluster placement group. This placement ensures single-digit microsecond latency and enhanced packet rate performance between instances.

Elastic IPs (Option B) do not influence internal networking. Jumbo frames (Option C) can marginally improve throughput but do not reduce propagation latency. Spread placement (Option D) increases distance, worsening latency.

Hence, Option A — using a cluster placement group — delivers the lowest possible network latency and is AWS’s best-practice design for HPC-style clusters.

The Instance Scheduler on AWS is an AWS-provided solution designed specifically to start and stop AWS resources such as Amazon RDS instances on a defined schedule. This directly aligns with the requirement to automatically manage RDS instances during predictable idle periods, such as weekends, to reduce costs.

RDS instances incur compute charges while running, even if idle. Stopping them during weekends eliminates those charges while retaining storage and backups. Instance Scheduler supports tag-based scheduling, centralized management, and automated start/stop workflows without custom scripting.

Option A introduces custom automation and ongoing maintenance overhead. Option C (Reserved Instances) is unsuitable because the databases are idle for long, predictable periods and Reserved Instances charge regardless of usage. Option D is incorrect because RDS does not support auto scaling of DB instance classes based on utilization.

Instance Scheduler is the most cost-effective and operationally efficient solution for this use case.

Layer 7 (application-layer) HTTP status codes such as 200, 404, and 500 are generated by web-facing services that process HTTP requests. In this architecture, both CloudFront and the Application Load Balancer (ALB) operate at Layer 7 and record HTTP response information in their access logs.

ALB access logs include detailed request and response data such as client IP address, request path, target response status code, and latency. These logs are essential for analyzing how backend EC2 instances respond to client requests.

CloudFront access logs record viewer requests and responses at the edge locations. These logs also include HTTP status codes returned to the client, making them critical for understanding end-user experience and edge-level behavior.

VPC Flow Logs capture network-level (Layer 3 and 4) traffic metadata such as source IP, destination IP, ports, and protocol. They do not contain HTTP status codes. AWS CloudTrail logs API calls to AWS services and does not capture application response codes. RDS logs contain database-related information, not HTTP responses.

Therefore, the correct sources for HTTP Layer 7 status codes are ALB access logs and CloudFront access logs.

An Auto Scaling warm pool keeps pre-initialized instances in a stopped or running state, allowing them to be quickly attached to the Auto Scaling group when scaling events occur. This significantly reduces scale-out latency caused by long bootstrapping scripts.

Unlike increasing the minimum instance count, warm pools do not permanently overprovision resources. Predictive scaling improves timing but does not eliminate boot time delays.

Therefore, warm pools provide the fastest scale-out with minimal cost overhead.

According to the AWS Cloud Operations and Compute documentation, when workloads exhibit predictable traffic patterns, the best practice is to use scheduled scaling for Amazon EC2 Auto Scaling groups.

With scheduled scaling, administrators can predefine the desired capacity of an Auto Scaling group to increase before anticipated demand (in this case, before the 2-hour peak) and scale back down afterward. This ensures that sufficient compute capacity is provisioned proactively, avoiding performance degradation while maintaining cost efficiency.

AWS notes: “Scheduled actions enable scaling your Auto Scaling group at predictable times, allowing you to pre-warm instances before demand spikes.”

Manual scaling (Option D) adds operational overhead. Adjusting launch templates (Option B) doesn’t affect scaling behavior, and permanently increasing minimum capacity (Option A) wastes resources outside of peak hours.

Thus, Option C provides an automated, cost-effective, and operationally efficient CloudOps solution.

CloudWatch Synthetics canaries require connectivity to both CloudWatch and Amazon S3 to function correctly. In a private VPC without internet access, AWS service access must be provided through VPC endpoints.

The canary needs to send metrics, logs, and execution data to CloudWatch, which requires an interface VPC endpoint for CloudWatch. It also needs to store artifacts such as screenshots and HAR files in Amazon S3, which requires a gateway VPC endpoint for S3. Without these endpoints, the canary cannot communicate with required AWS services and will fail to start.

DNS resolution and DNS hostnames must be enabled so the canary can resolve AWS service endpoints to the private IP addresses exposed by the VPC endpoints. This is a mandatory prerequisite for PrivateLink-based service access.

Option B and C incorrectly disable DNS functionality, which breaks service endpoint resolution. Option A includes invalid or irrelevant permissions and does not address private connectivity requirements.

Therefore, enabling DNS support and creating both the CloudWatch interface endpoint and the S3 gateway endpoint is the correct and complete solution.