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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Configuration Management and Infrastructure as Code | 22% | - Design and implement infrastructure as code
|
| Topic 2: Incident and Event Response | 18% | - Design and implement event and incident management
|
| Topic 3: High Availability and Disaster Recovery | 16% | - Implement data backup and restore strategies
|
| Topic 4: SDLC Automation | 22% | - Design and implement CI/CD pipelines
|
| Topic 5: Policies and Standards Automation | 10% | - Design and implement preventive and detective controls
|
| Topic 6: Monitoring and Logging | 12% | - Design and implement alerting and incident management
|
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NEW QUESTION # 192
A DevOps engineer is using AWS CodeDeploy across a fleet of Amazon EC2 instances in an EC2 Auto Scaling group. The associated CodeDeploy deployment group, which is integrated with EC2 Auto Scaling, is configured to perform in-place deployments with codeDeployDefault.oneAtATime During an ongoing new deployment, the engineer discovers that, although the overall deployment finished successfully, two out of five instances have the previous application revision deployed. The other three instances have the newest application revision What is likely causing this issue?
Answer: C
Explanation:
Explanation
When AWS CodeDeploy performs an in-place deployment, it updates the instances with the new application revision one at a time, as specified by the deployment configuration codeDeployDefault.oneAtATime. If a lifecycle event hook, such as AfterInstall, fails during the deployment, CodeDeploy will attempt to roll back to the previous version on the affected instances. This is likely what happened with the two instances that still have the previous application revision deployed. The failure of the AfterInstall lifecycle event hook triggered the rollback mechanism, resulting in those instances reverting to the previous application revision.
References:
* AWS CodeDeploy documentation on redeployment and rollback procedures1.
* Stack Overflow discussions on re-deploying older revisions with AWS CodeDeploy2.
* AWS CLI reference guide for deploying a revision2.
NEW QUESTION # 193
A company runs applications on Windows and Linux Amazon EC2 instances The instances run across multiple Availability Zones In an AWS Region. The company uses Auto Scaling groups for each application.
The company needs a durable storage solution for the instances. The solution must use SMB for Windows and must use NFS for Linux. The solution must also have sub-millisecond latencies. All instances will read and write the data.
Which combination of steps will meet these requirements? (Select THREE.)
Answer: A,B,E
Explanation:
* Create an Amazon Elastic File System (Amazon EFS) File System with Targets in Multiple Availability Zones:
Amazon EFS provides a scalable and highly available network file system that supports the NFS protocol. EFS is ideal for Linux instances as it allows multiple instances to read and write data concurrently.
Setting up EFS with targets in multiple Availability Zones ensures high availability and durability.
Reference:
* Create an Amazon FSx for NetApp ONTAP Multi-AZ File System:
Amazon FSx for NetApp ONTAP offers a fully managed file storage solution that supports both SMB for Windows and NFS for Linux.
The Multi-AZ deployment ensures high availability and durability, providing sub-millisecond latencies suitable for the application's performance requirements.
* Update the User Data for Each Application's Launch Template to Mount the File System:
Updating the user data in the launch template ensures that every new instance launched by the Auto Scaling group will automatically mount the appropriate file system.
This step is necessary to ensure that all instances can access the shared storage without manual intervention.
Example user data for mounting EFS (Linux)
#!/bin/bash
sudo yum install -y amazon-efs-utils
sudo mount -t efs fs-12345678:/ /mnt/efs
Example user data for mounting FSx (Windows):
By implementing these steps, the company can provide a durable storage solution with sub-millisecond latencies that supports both SMB and NFS protocols, meeting the requirements for both Windows and Linux instances.
Mounting EFS File Systems
Mounting Amazon FSx File Systems
NEW QUESTION # 194
A company uses an organization in AWS Organizations to manage multiple AWS accounts. The company needs a solution to detect sensitive information in Amazon S3 buckets in all the company's accounts. When the solution detects sensitive data, the solution must collect all the findings and make them available to the company's security officer in a single location. The solution must move S3 objects that contain sensitive information to a quarantine S3 bucket.
Which solutions will meet these requirements with the LEAST operational overhead? (Select TWO.)
Answer: A,C
Explanation:
The company requires an organization-wide, centralized, and automated solution to detect sensitive data in Amazon S3, aggregate findings in one location, and quarantine affected objects with minimal operational overhead. AWS provides a native service specifically designed for this purpose: Amazon Macie.
Option A is essential because Amazon Macie automatically discovers and classifies sensitive data such as PII in S3 buckets using managed machine learning models. When enabled at the organization level, Macie scans buckets across all accounts and Regions. Integrating Macie with AWS Security Hub centralizes all findings in a single dashboard, allowing the company's security officer to review and manage sensitive data alerts across the organization without building custom aggregation pipelines.
Detection alone is not sufficient; remediation is also required. Option C completes the solution by using Amazon EventBridge, which natively receives Macie findings in near real time. An EventBridge rule can trigger a Lambda function whenever Macie identifies sensitive data. The Lambda function can then copy the affected object to a quarantine S3 bucket and delete the original object, meeting the remediation requirement automatically and consistently.
Option D requires custom sensitive data detection logic, which is complex, error-prone, and unnecessary given Macie's capabilities. Option E is invalid because SCPs cannot inspect or move data. Option B does not detect sensitive information.
Therefore, A and C together provide the most efficient, scalable, and AWS-recommended solution.
NEW QUESTION # 195
A company recently migrated its application to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster that uses Amazon EC2 instances. The company configured the application to automatically scale based on CPU utilization.
The application produces memory errors when it experiences heavy loads. The application also does not scale out enough to handle the increased load. The company needs to collect and analyze memory metrics for the application over time.
Which combination of steps will meet these requirements? (Select THREE.)
Answer: C,D,F
Explanation:
* Step 1: Attaching the CloudWatchAgentServerPolicy to the IAM Role
The CloudWatch agent needs permissions to collect and send metrics, including memory metrics, to Amazon CloudWatch. You can attach the CloudWatchAgentServerPolicy managed IAM policy to the IAM instance profile or service account role to grant these permissions.
Action: Attach the CloudWatchAgentServerPolicy managed IAM policy to the IAM instance profile that the EKS cluster uses.
Why: This ensures the CloudWatch agent has the necessary permissions to collect memory metrics.
Reference:
This corresponds to Option A: Attach the CloudWatchAgentServerPolicy managed IAM policy to the IAM instance profile that the cluster uses.
* Step 2: Deploying the CloudWatch Agent to EC2 InstancesTo collect memory metrics from the EC2 instances running in the EKS cluster, the CloudWatch agent needs to be deployed on these instances. The agent collects system-level metrics, including memory usage.
Action: Deploy the unified Amazon CloudWatch agent to the existing EC2 instances in the EKS cluster. Update the Amazon Machine Image (AMI) for future instances to include the CloudWatch agent.
Why: The CloudWatch agent allows you to collect detailed memory metrics from the EC2 instances, which is not enabled by default.
This corresponds to Option C: Collect performance metrics by deploying the unified Amazon CloudWatch agent to the existing EC2 instances in the cluster. Add the agent to the AMI for any new EC2 instances that are added to the cluster.
* Step 3: Analyzing Memory Metrics Using Container InsightsAfter collecting the memory metrics, you can analyze them using the pod_memory_utilization metric in Amazon CloudWatch Container Insights. This metric provides visibility into the memory usage of the containers (pods) in the EKS cluster.
Action: Analyze the pod_memory_utilization CloudWatch metric in the Container Insights namespace by using the Service dimension.
Why: This provides detailed insights into memory usage at the container level, which helps diagnose memory-related issues.
This corresponds to Option E: Analyze the pod_memory_utilization Amazon CloudWatch metric in the Container Insights namespace by using the Service dimension.
NEW QUESTION # 196
A company uses Amazon API Gateway and AWS Lambda functions to implement an API. The company uses a pipeline in AWS CodePipeline to build and deploy the API. The pipeline contains a source stage, build stage, and deployment stage.
The company deploys the API without performing smoke tests. Soon after the deployment, the company observes multiple issues with the API. A security audit finds security vulnerabilities in the production code.
The company wants to prevent these issues from happening in the future.
Which combination of steps will meet this requirement? (Select TWO.)
Answer: B,C
NEW QUESTION # 197
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