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

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

AWS Lambda automatically publishes operational metrics to Amazon CloudWatch, including Duration, which represents the time a function invocation takes to run. To alert when processing time exceeds 10 seconds, the most direct and operationally efficient solution is to create a CloudWatch alarm on the Lambda Duration metric and configure the alarm action to publish to an SNS topic. This meets the monitoring requirement without requiring log parsing or additional query mechanisms.

Option B fits best because it uses the built-in metric stream for Lambda observability: CloudWatch metrics are near real-time, require minimal configuration, and alarms are a standard CloudOps practice for proactive notification. The alarm can be configured with the appropriate statistic (for example, Maximum or p99 via metric math where applicable) and a threshold of 10,000 milliseconds, ensuring the operations team is notified before performance degrades further.

Option A is incorrect because PostRuntimeExtensionsDuration is not the primary runtime metric for function execution time, and extracting runtime from logs is unnecessary. Option C changes the function timeout to 10 seconds, which would cause failures rather than simply notifying on slow executions. Option D is more operationally complex because it relies on log queries; CloudWatch alarms are more straightforward when a native metric exists.

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

To ensure users can launch instances only from approved AMIs, the control must be enforced at authorization time, not after the fact. An IAM policy with a condition that evaluates the resource tags on the AMI is the correct method. By using an aws:ResourceTag/ApprovedAMI condition (paired with allowing ec2:RunInstances only when the chosen AMI has the required tag/value), the organization can prevent launches from untagged or unapproved images. This implements preventative control and aligns with least privilege.

Option A (Organizations tag policy) is not an enforcement mechanism for EC2 API authorization; tag policies primarily help standardize tagging and report compliance rather than block API calls. Option C (AWS Config required-tags) evaluates resources for compliance after creation and cannot reliably prevent a launch at the time of the API call. Option D is unrelated; GuardDuty detections do not enforce AMI usage policy.

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 correct answer is C. Create a store image task. Specify the image ID and the destination S3 bucket, because this is the only AWS-supported solution that allows an AMI to be backed up to Amazon S3 and later restored while preserving the original AMI ID. This capability is essential for regulatory and compliance requirements where immutable identifiers must be retained.

According to AWS CloudOps documentation for Amazon EC2 AMI lifecycle management, the AMI store and restore feature is specifically designed for long-term retention, audit, and compliance scenarios. When an AMI is stored using a store image task, AWS packages the AMI’s configuration, metadata, and associated snapshots and saves them as encrypted objects in an Amazon S3 bucket. When the AMI is restored, AWS explicitly states that the restored AMI retains the same AMI ID as the original image, ensuring continuity for compliance tracking and operational dependencies.

Option A is incorrect because copying an AMI always results in a new AMI ID, even when copied within the same AWS Region. Option B is incorrect because archiving snapshots to Amazon S3 does not preserve AMI metadata or identity; restoring from snapshots requires creating a new AMI with a different ID. Option D is incorrect because the copy-image command is intended for cross-Region or cross-account AMI duplication and also generates a new AMI ID.

AWS CloudOps best practices clearly identify store image tasks as the correct mechanism when AMI identity preservation is required for governance, auditability, and compliance controls.

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

EC2 Instance Connect Endpoint (EIC Endpoint) enables SSH to instances in private subnets without public IPs and without needing to traverse the public internet. CloudOps guidance explains that you deploy the endpoint in the same VPC/subnet as the targets, then allow inbound SSH on the instance security group from the endpoint’s security group. Access is governed by IAM—administrators must have Instance Connect permissions; while the example uses a broad policy, the key mechanism is EIC in the private subnet plus SG rules scoped to the endpoint. Systems Manager Session Manager can provide shell access without SSH, but the requirement explicitly states “connect through SSH,” making EIC the purpose-built solution. Options B and D misuse Systems Manager for SSH and propose unnecessary SG changes or incorrect endpoint placement; Option C places the endpoint in a public subnet, which is not required for private SSH access. Therefore, creating an EC2 Instance Connect endpoint in the private subnet and updating SGs accordingly meets the requirement while keeping the instance non-internet-exposed.

The AWS Cloud Operations and Security documentation confirms that AWS WAF (Web Application Firewall) is designed to protect web applications from application-layer threats, including SQL injection, cross-site scripting (XSS), and other OWASP Top 10 vulnerabilities.

When integrated with an Application Load Balancer, AWS WAF inspects incoming traffic using rule groups. The AWS Managed Rules for SQL Injection Protection provide preconfigured, continuously updated filters that detect and block malicious SQL patterns.

AWS Shield (Standard or Advanced) defends against DDoS attacks, not application-layer SQL attacks, and vulnerability scanners (Option C) only detect, not prevent, exploitation.

Thus, Option D provides the correct, managed, and automated protection aligned with AWS best practices.

Per the AWS Cloud Operations, Networking, and Security documentation, the best practice for serving private S3 content securely to end users is to use Amazon CloudFront with Origin Access Control (OAC).

OAC enables CloudFront to access S3 buckets privately, even when Block Public Access settings are enabled at the account level. This allows content to be delivered globally and securely without making the S3 bucket public. The bucket policy explicitly allows access only from the CloudFront distribution, ensuring that users can retrieve PDF files only via CloudFront URLs.

This configuration offers:

Automatic scalability through CloudFront caching,

Improved security via private access control,

Minimal administration effort with fully managed services.

Other options require manual handling or make the bucket public, violating AWS security best practices.

Therefore, Option B—using CloudFront with Origin Access Control and a restrictive bucket policy—provides the most secure, efficient, and low-maintenance CloudOps solution.

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.

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

AWS Lambda automatically publishes operational metrics to Amazon CloudWatch, including Duration, which represents the time a function invocation takes to run. To alert when processing time exceeds 10 seconds, the most direct and operationally efficient solution is to create a CloudWatch alarm on the Lambda Duration metric and configure the alarm action to publish to an SNS topic. This meets the monitoring requirement without requiring log parsing or additional query mechanisms.

Option B fits best because it uses the built-in metric stream for Lambda observability: CloudWatch metrics are near real-time, require minimal configuration, and alarms are a standard CloudOps practice for proactive notification. The alarm can be configured with the appropriate statistic (for example, Maximum or p99 via metric math where applicable) and a threshold of 10,000 milliseconds, ensuring the operations team is notified before performance degrades further.

Option A is incorrect because PostRuntimeExtensionsDuration is not the primary runtime metric for function execution time, and extracting runtime from logs is unnecessary. Option C changes the function timeout to 10 seconds, which would cause failures rather than simply notifying on slow executions. Option D is more operationally complex because it relies on log queries; CloudWatch alarms are more straightforward when a native metric exists.

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.