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Amazon AWS Certified CloudOps Engineer - Associate Sample Questions (Q71-Q76):

NEW QUESTION # 71
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?

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 # 72
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?

Answer: A

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:* 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 # 73
A company has an AWS CloudFormation template that includes an AWS::EC2::Instance resource and a custom resource (Lambda function). The Lambda function fails because it runs before the EC2 instance is launched.
Which solution will resolve this issue?

Answer: A

Explanation:
The AWS Cloud Operations and Infrastructure-as-Code documentation specifies that when using AWS CloudFormation, resources are created in parallel by default unless explicitly ordered using DependsOn.
If a custom resource (Lambda) depends on another resource (like an EC2 instance) to exist before execution, a DependsOn attribute must be added to enforce creation order. This ensures the EC2 instance is launched and available before the custom resource executes its automation logic.
Updating the service token (Option B) doesn't affect order of execution. The cfn-response module (Option C) handles callback communication but not sequencing. Fn::If (Option D) is for conditional creation, not dependency control.
Therefore, Option A is correct - adding a DependsOn attribute guarantees that CloudFormation provisions the EC2 instance before executing the Lambda custom resource.
Reference: AWS Cloud Operations & Infrastructure-as-Code Guide - Using DependsOn for Resource Creation Order in CloudFormation Templates


NEW QUESTION # 74
A CloudOps engineer is troubleshooting an AWS CloudFormation stack creation that failed. Before the CloudOps engineer can identify the problem, the stack and its resources are deleted. For future deployments, the CloudOps engineer must preserve any resources that CloudFormation successfully created.
What should the CloudOps engineer do to meet this requirement?

Answer: A

Explanation:
By default, when AWS CloudFormation encounters a failure during stack creation, it automatically rolls back and deletes any resources that were successfully created. This behavior makes troubleshooting difficult because the failed and partially created resources are no longer available for inspection.
CloudFormation provides the OnFailure parameter to control this behavior. Setting the parameter to DO_NOTHING instructs CloudFormation to stop stack creation when a failure occurs and retain all successfully created resources. This allows the CloudOps engineer to inspect the environment, review logs, and identify the root cause without redeploying resources.
The DisableRollback parameter controls rollback behavior but does not provide the same explicit behavior control during failure scenarios. Rollback triggers are used for monitoring-based rollback, not for preserving resources on failure. Setting OnFailure to ROLLBACK explicitly enforces deletion, which is the opposite of the requirement.
Therefore, setting the OnFailure parameter to DO_NOTHING is the correct solution.


NEW QUESTION # 75
A company has users that deploy Amazon EC2 instances with more Amazon EBS performance capacity than required. A CloudOps engineer must review all EBS volumes and create cost optimization recommendations based on IOPS and throughput.
What should the CloudOps engineer do in the MOST operationally efficient way?

Answer: D

Explanation:
AWS Compute Optimizer analyzes historical utilization metrics and provides rightsizing recommendations for EBS volumes, including IOPS and throughput. It is fully managed, continuously updated, and requires minimal operational effort.
Manual reviews and benchmarking do not scale. Changing instance types does not address EBS overprovisioning.
Therefore, AWS Compute Optimizer is the best solution.


NEW QUESTION # 76
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