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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security and Compliance Automation | 13% | - Security automation in CI/CD and infrastructure
|
| Topic 2: SDLC Automation | 22% | - CI/CD pipeline design and implementation
|
| Topic 3: Resilient Cloud Solutions | 15% | - High availability and fault tolerance design
|
| Topic 4: Configuration Management and Infrastructure as Code | 17% | - Infrastructure provisioning and automation
|
| Topic 5: Incident and Event Management | 18% | - Operational response and recovery
|
| Topic 6: Monitoring and Logging | 15% | - Observability and metrics
|
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NEW QUESTION # 449
A company uses an AWS CodeArtifact repository to store Python packages that the company developed internally. A DevOps engineer needs to use AWS CodeDeploy to deploy an application to an Amazon EC2 instance. The application uses a Python package that is stored in the CodeArtifact repository. A BeforeInstall lifecycle event hook will install the package.
The DevOps engineer needs to grant the EC2 instance access to the CodeArtifact repository.
Which solution will meet this requirement?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To allow an EC2 instance to access CodeArtifact, an IAM role attached via an instance profile must be granted permissions to access the CodeArtifact repository. The EC2 instance assumes this role.
The instance then uses the AWS CLI command aws codeartifact login to authenticate and configure the package manager (e.g., pip) to use the CodeArtifact repository. This command obtains an authorization token and sets up repository credentials securely on the instance.
Service-linked roles (Option A) are managed by AWS services, not used for instance access. CodeArtifact does not support ACLs (Option C), and resource-based policies (Option B) do not grant access to EC2 instances by principal.
This method is standard for securely managing credentials and access to CodeArtifact in automated deployments.
Reference:
AWS CodeArtifact Access Control and Authentication:
"Use IAM roles attached to compute resources and the aws codeartifact login command to authenticate to repositories." (AWS CodeArtifact Developer Guide)
NEW QUESTION # 450
A company's application has an API that retrieves workload metrics. The company needs to audit, analyze, and visualize these metrics from the application to detect issues at scale.
Which combination of steps will meet these requirements? (Select THREE).
Answer: B,E,F
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The recommended architecture to audit, analyze, and visualize application workload metrics at scale involves:
* UsingAmazon EventBridgeto scheduleAWS Lambdainvocations that call the application API and fetch metrics (Option A). The data is stored inAmazon S3, which is ideal for scalable, cost-effective storage of large datasets.
* Cataloging the stored data withAWS Glue crawlers, enabling schema discovery and making data queryable viaAmazon Athena(Option C).
* Visualizing the data by creatingAmazon QuickSight datasetsfrom Athena views and building dashboards for analysis (Option E).Option B and D introduce DynamoDB, which is less suitable for large-scale analytics and Athena querying. Option F suggests querying Athena via Lambda widgets in CloudWatch, which adds complexity without significant benefit over QuickSight.
Reference:
Serverless Analytics Architecture on AWS:"Use Lambda + S3 + Glue + Athena + QuickSight for scalable data ingestion, cataloging, querying, and visualization."(AWS Analytics Reference Architecture) AWS Glue Crawlers and Athena:"Glue crawlers catalog data stored in S3 and create Athena tables to enable SQL querying."(AWS Glue Documentation) Amazon QuickSight for Visualization:"QuickSight enables creating interactive dashboards from Athena query results."(Amazon QuickSight Overview)
NEW QUESTION # 451
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: C,E,F
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: Amazon EFS Overview
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.
Reference: Amazon FSx for NetApp ONTAP
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.
References:
Mounting EFS File Systems
Mounting Amazon FSx File Systems
NEW QUESTION # 452
A DevOps engineer is building a multistage pipeline with AWS CodePipeline to build, verify, stage, test, and deploy an application. A manual approval stage is required between the test stage and the deploy stage. The development team uses a custom chat tool with webhook support that requires near-real-time notifications.
How should the DevOps engineer configure status updates for pipeline activity and approval requests to post to the chat tool?
Answer: A
NEW QUESTION # 453
A company has an application that runs on Amazon EC2 instances in an Auto Scaling group. The application processes a high volume of messages from an Amazon Simple Queue Service (Amazon SQS) queue.
A DevOps engineer noticed that the application took several hours to process a group of messages from the SQS queue. The average CPU utilization of the Auto Scaling group did not cross the threshold of a target tracking scaling policy when processing the messages. The application that processes the SQS queue publishes logs to Amazon CloudWatch Logs.
The DevOps engineer needs to ensure that the queue is processed quickly.
Which solution meets these requirements with the LEAST operational overhead?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The default CPU utilization metric does not reflect the processing backlog in the SQS queue, so the Auto Scaling group is not scaling properly to handle the workload.
To scale the Auto Scaling group based on queue length, you can create a target tracking scaling policy that uses a custom metric that combines the SQS queue's ApproximateNumberOfMessagesVisible and the number of instances (GroupIn-ServiceInstances) metric using CloudWatch metric math. This allows the scaling policy to calculate the average number of messages per instance and scale accordingly.
This approach requires no additional Lambda functions or log processing, thus minimizing operational overhead.
Option A and B require Lambda functions to publish custom metrics, which increases operational complexity. Option D also adds complexity with logging and metric filters.
Reference:
Scaling based on SQS queue length using metric math:
"You can create CloudWatch metric math expressions combining SQS and Auto Scaling group metrics to enable target tracking scaling policies that respond to queue backlog." (AWS Auto Scaling with SQS) Target Tracking Scaling Policies:
"Target tracking policies can use metric math expressions as a source to make scaling decisions." (AWS Auto Scaling Target Tracking)
NEW QUESTION # 454
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