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NEW QUESTION # 31
An AWS Lambda function is intermittently failing several times a day. A CloudOps engineer must find out how often this error occurred in the last 7 days.
Which action will meet this requirement in the MOST operationally efficient manner?
- A. Use Amazon OpenSearch Service to stream the Amazon CloudWatch logs for the Lambda function.
- B. Use Amazon Athena to query the Amazon CloudWatch logs that are associated with the Lambda function.
- C. Use Amazon Athena to query the AWS CloudTrail logs that are associated with the Lambda function.
- D. Use Amazon CloudWatch Logs Insights to query the associated Lambda function logs.
Answer: D
Explanation:
The AWS Cloud Operations and Monitoring documentation states that Amazon CloudWatch Logs Insights provides a purpose-built query engine for analyzing and visualizing log data directly within CloudWatch. For Lambda, all invocation results (including errors) are automatically logged to CloudWatch Logs.
By querying these logs with CloudWatch Logs Insights, the CloudOps engineer can efficiently count the number of "ERROR" or "Exception" occurrences over the past 7 days using simple SQL-like commands. This method is serverless, cost-efficient, and real-time.
Athena (Options A and B) would require exporting data to Amazon S3, and OpenSearch (Option D) adds unnecessary operational complexity.
Thus, Option C provides the most efficient and native AWS CloudOps approach for rapid Lambda error analysis.
NEW QUESTION # 32
A CloudOps engineer configures an application to run on Amazon EC2 instances behind an Application Load Balancer (ALB) in a simple scaling Auto Scaling group with the default settings. The Auto Scaling group is configured to use the RequestCountPerTarget metric for scaling. The CloudOps engineer notices that the RequestCountPerTarget metric exceeded the specified limit twice in 180 seconds.
How will the number of EC2 instances in this Auto Scaling group be affected in this scenario?
- A. The Auto Scaling group will launch an additional EC2 instance every time the RequestCountPerTarget metric exceeds the predefined limit.
- B. The Auto Scaling group will send an alert to the ALB to rebalance the traffic and not add new EC2 instances until the load is normalized.
- C. The Auto Scaling group will launch one EC2 instance and will wait for the default cooldown period before launching another instance.
- D. The Auto Scaling group will try to distribute the traffic among all EC2 instances before launching another instance.
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Doocuments:
With simple scaling policies, an Auto Scaling group performs one scaling activity when the alarm condition is met, then observes a default cooldown period (300 seconds) before another scaling activity of the same type can begin. CloudOps guidance explains that cooldown prevents rapid successive scale-outs by allowing time for the newly launched instance(s) to register with the load balancer and impact the metric. Even if the alarm breaches multiple times during the cooldown window, the group waits until the cooldown completes before evaluating and acting again. In this case, although RequestCountPerTarget exceeded the threshold twice within 180 seconds, the group will launch a single instance and then wait for cooldown before any additional scale-out can occur. Options A, C, and D do not reflect the behavior of simple scaling with cooldowns; A describes step/target-tracking-like behavior, and C/D are not Auto Scaling mechanics.
References (AWS CloudOps Documents / Study Guide):
* Amazon EC2 Auto Scaling - Simple Scaling Policies and Cooldown (User Guide)
* Elastic Load Balancing Metrics - ALB RequestCountPerTarget (CloudWatch Metrics)
* AWS Well-Architected Framework - Performance Efficiency & Operational Excellence
NEW QUESTION # 33
An application runs on Amazon EC2 instances that are in an Auto Scaling group. A CloudOps engineer needs to implement a solution that provides a central storage location for errors that the application logs to disk. The solution must also provide an alert when the application logs an error.
What should the CloudOps engineer do to meet these requirements?
- A. Create an Auto Scaling lifecycle hook that invokes an EC2-based script to identify errors. Configure the script to push the error messages to an Amazon CloudWatch log group when the EC2 instances scale in. Create a CloudWatch alarm that publishes to an Amazon Simple Notification Service (Amazon SNS) topic that has an email subscription when the number of error messages exceeds a threshold.
- B. Deploy and configure the Amazon CloudWatch agent on the EC2 instances to log to a CloudWatch log group. Create a metric filter on the target CloudWatch log group. Create a CloudWatch alarm that publishes to an Amazon Simple Notification Service (Amazon SNS) topic that has an email subscription.
- C. Create a cron job on the EC2 instances to identify errors and push the errors to an Amazon CloudWatch metric filter. Configure the filter to publish to an Amazon Simple Notification Service (Amazon SNS) topic that has an SMS subscription.
- D. Deploy an AWS Lambda function that pushes the errors directly to Amazon CloudWatch Logs. Configure the Lambda function to run every time the log file is updated on disk.
Answer: B
Explanation:
The AWS Cloud Operations and Monitoring documentation specifies that the Amazon CloudWatch Agent is the recommended tool for collecting system and application logs from EC2 instances. The agent pushes these logs into a centralized CloudWatch Logs group, providing durable storage and real-time monitoring.
Once the logs are centralized, a CloudWatch Metric Filter can be configured to search for specific error keywords (for example, "ERROR" or "FAILURE"). This filter transforms matching log entries into custom metrics. From there, a CloudWatch Alarm can monitor the metric threshold and publish notifications to an Amazon SNS topic, which can send email or SMS alerts to subscribed recipients.
This combination provides a fully automated, managed, and serverless solution for log aggregation and error alerting. It eliminates the need for manual cron jobs (Option B), custom scripts (Option D), or Lambda-based log streaming (Option C).
NEW QUESTION # 34
A company uses AWS Organizations to manage multiple AWS accounts. A CloudOps engineer must identify all IPv4 ports open to 0.0.0.0/0 across the organization's accounts.
Which solution will meet this requirement with the LEAST operational effort?
- A. Review AWS Trusted Advisor findings in an organizational view for the Security Groups - Specific Ports Unrestricted check.
- B. Enable Amazon Inspector in each account. Run an automated workload discovery job.
- C. Create an AWS Lambda function to gather security group rules from all accounts. Aggregate the findings in an Amazon S3 bucket.
- D. Use the AWS CLI to print all security group rules for review.
Answer: A
Explanation:
According to AWS Cloud Operations and Governance documentation, AWS Trusted Advisor provides automated checks for security group rules across all accounts, including identifying ports open to 0.0.0.0/0.
When viewed in organizational mode, Trusted Advisor integrates with AWS Organizations, allowing administrators to access organization-wide security findings from a central management account. This approach requires no custom code, additional infrastructure, or manual inspection, providing immediate visibility and the lowest operational overhead.
AWS CLI scripts (Option A) or Lambda automation (Option C) introduce additional maintenance, and Amazon Inspector (Option D) is focused on instance-level vulnerabilities, not network access rules.
Therefore, Option B is the AWS-recommended CloudOps best practice for centralized and low-effort open-port auditing.
NEW QUESTION # 35
An AWS CloudFormation template creates an Amazon RDS instance. This template is used to build up development environments as needed and then delete the stack when the environment is no longer required. The RDS-persisted data must be retained for further use, even after the CloudFormation stack is deleted.
How can this be achieved in a reliable and efficient way?
- A. Create a new CloudFormation template to perform backups of the RDS instance, and run this template before deleting the stack.
- B. Write a script to continue backing up the RDS instance every five minutes.
- C. Use the Snapshot Deletion Policy in the CloudFormation template definition of the RDS instance.
- D. Create an AWS Lambda function to take a snapshot of the RDS instance, and manually invoke the function before deleting the stack.
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of AWS CloudOps Doocuments:
AWS CloudFormation supports the DeletionPolicy attribute to control what happens to a resource when a stack is deleted. For Amazon RDS DB instances, setting DeletionPolicy: Snapshot instructs CloudFormation to retain a final DB snapshot automatically at stack deletion. CloudOps best practice recommends using this native mechanism for data retention and auditability, avoiding manual scripts or out-of-band processes. Options A, B, and D introduce operational overhead and potential human error. With DeletionPolicy set to Snapshot, the environment can be repeatedly created and torn down while preserving data states for later restoration with minimal manual steps. This aligns with IaC principles-declarative, repeatable, and reliable-and supports efficient lifecycle management of ephemeral development stacks.
References (AWS CloudOps Documents / Study Guide):
* AWS Certified CloudOps Engineer - Associate (SOA-C03) Exam Guide - Deployment, Provisioning and Automation
* AWS CloudFormation User Guide - DeletionPolicy Attribute (Snapshot for RDS)
* AWS Well-Architected Framework - Operational Excellence Pillar
NEW QUESTION # 36
A CloudOps engineer must manage the security of an AWS account. Recently, an IAM user's access key was mistakenly uploaded to a public code repository. The engineer must identify everything that was changed using this compromised key.
How should the CloudOps engineer meet these requirements?
- A. Create an Amazon EventBridge rule to send all IAM events to an AWS Lambda function for analysis.
- B. Search AWS CloudTrail event history for all events initiated with the compromised access key within the suspected timeframe.
- C. Search VPC Flow Logs for all events initiated with the compromised access key within the suspected timeframe.
- D. Query Amazon EC2 logs by using Amazon CloudWatch Logs Insights for all events initiated with the compromised access key within the suspected timeframe.
Answer: B
Explanation:
According to the AWS Cloud Operations and Security documentation, AWS CloudTrail is the authoritative service for recording API activity across all AWS services within an account.
When an access key is compromised, CloudTrail logs all API requests made using that key, including details such as:
The user identity (access key ID) that made the request,
The service, operation, resource, and timestamp affected, and
The source IP address and region of the request.
By searching the CloudTrail event history for the specific access key ID, the CloudOps engineer can identify every action performed by that key during the suspected breach window.
Other options are incorrect:
EventBridge (A) is event-driven, not historical.
CloudWatch Logs (B) monitors system logs, not AWS API activity.
VPC Flow Logs (D) track network-level traffic, not API calls.
Therefore, the correct solution is Option C - using AWS CloudTrail event history to audit and trace all actions executed via the compromised access key.
NEW QUESTION # 37
A CloudOps engineer needs to ensure that AWS resources across multiple AWS accounts are tagged consistently. The company uses an organization in AWS Organizations to centrally manage the accounts. The company wants to implement cost allocation tags to accurately track the costs that are allocated to each business unit.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Configure AWS CloudTrail events to invoke an AWS Lambda function to detect untagged resources and to automatically assign tags based on predefined rules.
- B. Use AWS Config to evaluate tagging compliance. Use AWS Budgets to apply tags for cost allocation.
- C. Use AWS Service Catalog to provision only pre-tagged resources. Use AWS Trusted Advisor to enforce tagging across the organization.
- D. Use Organizations tag policies to enforce mandatory tagging on all resources. Enable cost allocation tags in the AWS Billing and Cost Management console.
Answer: D
Explanation:
Tagging is essential for governance, cost management, and automation in CloudOps operations. The AWS Organizations tag policies feature allows centralized definition and enforcement of required tag keys and accepted values across all accounts in an organization. According to the AWS CloudOps study guide under Deployment, Provisioning, and Automation, tag policies enable automatic validation of tags, ensuring consistency with minimal manual overhead.
Once tagging consistency is enforced, enabling cost allocation tags in the AWS Billing and Cost Management console allows accurate cost distribution per business unit. AWS documentation states:
"Use AWS Organizations tag policies to standardize tags across accounts. You can activate cost allocation tags in the Billing console to track and allocate costs." Option B introduces unnecessary complexity with Lambda automation. Option C detects but does not enforce tagging. Option D limits flexibility to Service Catalog resources only. Therefore, Option A provides a centrally managed, automated, and low-overhead solution that meets CloudOps tagging and cost-tracking requirements.
References (AWS CloudOps Documents / Study Guide):
* AWS Certified CloudOps Engineer - Associate (SOA-C03) Exam Guide - Domain 3: Deployment, Provisioning and Automation
* AWS Organizations - Tag Policies
* AWS Billing and Cost Management - Cost Allocation Tags
* AWS Well-Architected Framework - Operational Excellence and Cost Optimization Pillars
NEW QUESTION # 38
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 AWS CloudTrail to capture generative AI application logs. Stream the logs to Amazon Managed Service for Apache Flink. Use SQL queries to generate reports.
- B. Use Amazon CloudWatch to capture generative AI application logs. Stream the logs to Amazon OpenSearch Service. Use an OpenSearch dashboard visualization 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.
Answer: C
Explanation:
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.
NEW QUESTION # 39
A company with millions of subscribers needs to automatically send notifications every Saturday. The company already uses Amazon SNS to send messages but has historically sent them manually.
Which solution will meet these requirements in the MOST operationally efficient way?
- A. Launch a new Amazon EC2 instance. Configure a cron job to use the AWS SDK to send an SNS notification to subscribers every Saturday.
- B. Create an SNS subscription to a message fanout that sends notifications to subscribers every Saturday.
- C. Create a rule in Amazon EventBridge that triggers every Saturday. Configure the rule to publish a notification to an SNS topic.
- D. Use AWS Step Functions scheduling to run a step every Saturday. Configure the step to publish a message to an SNS topic.
Answer: C
Explanation:
Per the AWS Cloud Operations and Event Management documentation, Amazon EventBridge provides native scheduling capabilities that can trigger events at defined intervals-such as weekly, daily, or cron-based schedules.
Creating an EventBridge rule that runs every Saturday and publishes a message to an SNS topic fully automates the notification process without maintaining servers or manual jobs. This approach is serverless, highly reliable, and fully managed by AWS.
By contrast:
EC2 cron jobs (Option A) require instance management, patching, and cost overhead.
SNS subscriptions (Option C) handle message delivery, not scheduling.
Step Functions (Option D) are designed for complex workflows, not simple scheduled triggers.
Thus, Option B provides the most operationally efficient CloudOps solution by integrating EventBridge scheduled events with SNS topics for automated, recurring notifications.
NEW QUESTION # 40
A company is using an Amazon Aurora MySQL DB cluster that has point-in-time recovery, backtracking, and automatic backup enabled. A CloudOps engineer needs to roll back the DB cluster to a specific recovery point within the previous 72 hours. Restores must be completed in the same production DB cluster.
Which solution will meet these requirements?
- A. Create an Aurora Replica. Promote the replica to replace the primary DB instance.
- B. Use backtracking to rewind the existing DB cluster to the desired recovery point.
- C. Create an AWS Lambda function to restore an automatic backup to the existing DB cluster.
- D. Use point-in-time recovery to restore the existing DB cluster to the desired recovery point.
Answer: B
Explanation:
As documented in AWS Cloud Operations and Database Recovery, Aurora Backtrack allows you to rewind the existing database cluster to a chosen point in time without creating a new cluster. This feature supports fine-grained rollback for accidental data changes, making it ideal for scenarios like table deletions or logical corruption.
Backtracking maintains continuous transaction logs and permits rewinding within a configurable window (up to 72 hours). It does not require creating a new cluster or endpoint, and it preserves the same production environment, fulfilling the operational requirement for in-place recovery.
In contrast, Point-in-Time Recovery (Option D) always creates a new cluster, while replica promotion (Option A) and Lambda restoration (Option B) are unrelated to immediate rollback operations.
Therefore, Option C, using Aurora Backtrack, best meets the requirement for same-cluster restoration and minimal downtime.
NEW QUESTION # 41
A company has an application running on EC2 that stores data in an Amazon RDS for MySQL Single-AZ DB instance. The application requires both read and write operations, and the company needs failover capability with minimal downtime.
Which solution will meet these requirements?
- A. Add the DB instance to an Auto Scaling group that has a minimum capacity of 2 and a desired capacity of 2.
- B. Modify the DB instance to be a Multi-AZ DB instance deployment.
- C. Use RDS Proxy to configure a proxy in front of the DB instance.
- D. Add a read replica in the same Availability Zone where the DB instance is deployed.
Answer: B
Explanation:
According to the AWS Cloud Operations and Database Reliability documentation, Amazon RDS Multi-AZ deployments provide high availability and automatic failover by maintaining a synchronous standby replica in a different Availability Zone.
In the event of instance failure, planned maintenance, or Availability Zone outage, Amazon RDS automatically promotes the standby to primary with minimal downtime (typically less than 60 seconds). The failover is transparent to applications because the DB endpoint remains the same.
By contrast, read replicas (Option B) are asynchronous and do not provide automated failover. Auto Scaling (Option C) applies to EC2, not RDS. RDS Proxy (Option D) improves connection management but does not add redundancy.
Thus, Option A - converting the RDS instance into a Multi-AZ deployment - delivers the required high availability and business continuity with minimal operational effort.
NEW QUESTION # 42
A company runs an application on Amazon EC2 that connects to an Amazon Aurora PostgreSQL database. A developer accidentally drops a table from the database, causing application errors. Two hours later, a CloudOps engineer needs to recover the data and make the application functional again.
Which solution will meet this requirement?
- A. Perform a point-in-time recovery on the existing database to restore the database to a specified point in time, 2 hours in the past.
- B. Create a new Aurora cluster. Choose the Restore data from S3 bucket option. Choose log files up to the failure time 2 hours in the past.
- C. Perform a point-in-time recovery and create a new database to restore the database to a specified point in time, 2 hours in the past. Reconfigure the application to use a new database endpoint.
- D. Use the Aurora Backtrack feature to rewind the database to a specified time, 2 hours in the past.
Answer: C
Explanation:
In the AWS Cloud Operations and Aurora documentation, when data loss occurs due to human error such as dropped tables, Point-in-Time Recovery (PITR) is the recommended method for restoration. PITR creates a new Aurora cluster restored to a specific time before the failure.
The restored cluster has a new endpoint that must be reconfigured in the application to resume normal operations. AWS does not support performing PITR directly on an existing production database because that would overwrite current data.
Aurora Backtrack (Option A) applies only to Aurora MySQL, not PostgreSQL. Option B is incorrect because PITR cannot be executed in place. Option D refers to an import process from S3, which is unrelated to time-based recovery.
Hence, Option C is correct and follows the AWS CloudOps standard recovery pattern for PostgreSQL workloads.
NEW QUESTION # 43
A company requires the rotation of administrative credentials for production workloads on a regular basis. A CloudOps engineer must implement this policy for an Amazon RDS DB instance's master user password.
Which solution will meet this requirement with the LEAST operational effort?
- A. Create a new RDS database secret in AWS Secrets Manager. Apply the secret to the RDS DB instance. Configure automatic rotation.
- B. Create a new String parameter in AWS Systems Manager Parameter Store. Configure automatic rotation.
- C. Create an AWS Lambda function to change the RDS master user password. Create an Amazon EventBridge scheduled rule to invoke the Lambda function.
- D. Create a new SecureString parameter in AWS Systems Manager Parameter Store. Encrypt the parameter with an AWS Key Management Service (AWS KMS) key. Configure automatic rotation.
Answer: A
Explanation:
AWS Secrets Manager natively supports credential management and automatic rotation for Amazon RDS master user passwords. When a secret is associated with an RDS instance, Secrets Manager automatically updates the password both in the secret and on the database, without downtime or manual scripting.
AWS documentation confirms:
"AWS Secrets Manager can automatically rotate the master user password for Amazon RDS databases. Rotation is fully managed and integrated, requiring no custom code or maintenance." Option A introduces unnecessary Lambda automation. Option B and C use Parameter Store, which does not provide direct RDS password rotation. Therefore, Option D achieves secure, automatic credential rotation with least operational effort, fully aligned with CloudOps security automation principles.
References (AWS CloudOps Documents / Study Guide):
* AWS Certified CloudOps Engineer - Associate (SOA-C03) Exam Guide - Domain 4: Security and Compliance
* AWS Secrets Manager - Rotating Secrets for Amazon RDS
* AWS Well-Architected Framework - Security Pillar
* Amazon RDS User Guide - Managing Master User Passwords
NEW QUESTION # 44
A company runs applications on Amazon EC2 instances. The company wants to ensure that SSH ports on the EC2 instances are never open. The company has enabled AWS Config and has set up the restricted-ssh AWS managed rule.
A CloudOps engineer must implement a solution to remediate SSH port access for noncompliant security groups.
What should the engineer do to meet this requirement with the MOST operational efficiency?
- A. Configure the AWS Config rule to identify noncompliant security groups. Configure the rule to use the AWS-DisableIncomingSSHOnPort22 AWS Systems Manager Automation runbook to remediate noncompliant resources.
- B. Make an AWS Config API call to search for noncompliant security groups. Disable SSH access for noncompliant security groups by using a Deny rule.
- C. Configure the AWS Config rule to identify noncompliant security groups. Configure the rule to use the AWS-PublishSNSNotification AWS Systems Manager Automation runbook to send notifications about noncompliant resources.
- D. Configure the AWS Config rule to identify noncompliant security groups. Manually update each noncompliant security group to remove the Allow rule.
Answer: A
Explanation:
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.
NEW QUESTION # 45
A CloudOps engineer creates an AWS CloudFormation template to define an application stack that can be deployed in multiple AWS Regions. The CloudOps engineer also creates an Amazon CloudWatch dashboard by using the AWS Management Console. Each deployment of the application requires its own CloudWatch dashboard.
How can the CloudOps engineer automate the creation of the CloudWatch dashboard each time the application is deployed?
- A. Update the CloudFormation template to define an AWS::CloudWatch::Dashboard resource. Specify the name of the existing dashboard in the DashboardName property.
- B. Update the CloudFormation template to define an AWS::CloudWatch::Dashboard resource. Use the intrinsic Ref function to reference the ID of the existing CloudWatch dashboard.
- C. Create a script by using the AWS CLI to run the aws cloudformation put-dashboard command with the name of the dashboard. Run the command each time a new CloudFormation stack is created.
- D. Export the existing CloudWatch dashboard as JSON. Update the CloudFormation template to define an AWS::CloudWatch::Dashboard resource. Include the exported JSON in the resource's DashboardBody property.
Answer: D
Explanation:
According to CloudOps automation and monitoring best practices, CloudWatch dashboards should be provisioned as infrastructure-as-code (IaC) resources using AWS CloudFormation to ensure consistency, repeatability, and version control. AWS CloudFormation supports the AWS::CloudWatch::Dashboard resource, where the DashboardBody property accepts a JSON object describing widgets, metrics, and layout.
By exporting the existing dashboard configuration as JSON and embedding it into the CloudFormation template, every deployment of the application automatically creates its corresponding dashboard. This method aligns with the CloudOps requirement for automated deployment and operational visibility within the same stack lifecycle.
AWS documentation explicitly states:
"Use the AWS::CloudWatch::Dashboard resource to create a dashboard from your template. You can include the same JSON you use to define a dashboard in the console." Option A requires manual execution. Options C and D incorrectly reference or reuse existing dashboards, failing to produce unique, deployment-specific dashboards.
References (AWS CloudOps Documents / Study Guide):
* AWS Certified CloudOps Engineer - Associate (SOA-C03) Exam Guide - Domain 1: Monitoring and Logging
* AWS CloudFormation User Guide - Resource Type: AWS::CloudWatch::Dashboard
* AWS Well-Architected Framework - Operational Excellence Pillar
* Amazon CloudWatch - Automating Dashboards with Infrastructure as Code
NEW QUESTION # 46
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