Free Practice Questions for Amazon Web Services DOP-C02 Exam

The latency in processing and ingesting logs can be caused by several factors, such as the throughput of the Kinesis data stream, the concurrency of the Lambda function, and the configuration of the event source mapping. To reduce the latency, the following steps can be taken:

Create a data stream consumer with enhanced fan-out. Set the Lambda function that processes the logs as the consumer. This will allow the Lambda function to receive records from the data stream with dedicated throughput of up to 2 MB per second per shard, independent of other consumers1. This will reduce the contention and delay in accessing the data stream.

Increase the ParallelizationFactor setting in the Lambda event source mapping. This will allow the Lambda service to invoke more instances of the function concurrently to process the records from the data stream2. This will increase the processing capacity and reduce the backlog of records in the data stream.

Configure reserved concurrency for the Lambda function that processes the logs. This will ensure that the function has enough concurrency available to handle the increased load from the data stream3. This will prevent the function from being throttled by the account-level concurrency limit.

The other options are not effective or may have negative impacts on the latency. Option D is not suitable because increasing the batch size in the Kinesis data stream will increase the amount of data that the Lambda function has to process in each invocation, which may increase the execution time and latency4. Option E is not advisable because turning off the ReportBatchItemFailures setting in the Lambda event source mapping will prevent the Lambda service from retrying the failed records, which may result in data loss. Option F is not necessary because increasing the number of shards in the Kinesis data stream will increase the throughput of the data stream, but it will not affect the processing speed of the Lambda function, which is the bottleneck in this scenario.

1: Using AWS Lambda with Amazon Kinesis Data Streams - AWS Lambda

2: AWS Lambda event source mappings - AWS Lambda

3: Managing concurrency for a Lambda function - AWS Lambda

4: AWS Lambda function scaling - AWS Lambda

AWS Lambda event source mappings - AWS Lambda

Scaling Amazon Kinesis Data Streams with AWS CloudFormation - Amazon Kinesis Data Streams

 Turn off cross-zone load balancing on the ALB ' s target group:

Cross-zone load balancing distributes traffic evenly across all registered targets in all enabled Availability Zones. Turning this off will ensure that each target group only handles requests from its respective Availability Zone.

To disable cross-zone load balancing:

Go to the Amazon EC2 console.

Navigate to Load Balancers and select the ALB.

Choose the Target Groups tab, select the target group, and then select the Group details tab.

Click on Edit and turn off Cross-zone load balancing.

 Use Amazon Route 53 Application Recovery Controller to start a zonal shift away from the Availability Zone:

Amazon Route 53 Application Recovery Controller provides the ability to control traffic flow to ensure high availability and disaster recovery.

By using Route 53 Application Recovery Controller, you can perform a zonal shift to redirect traffic away from the unhealthy Availability Zone.

To start a zonal shift:

Configure Route 53 Application Recovery Controller by creating a cluster and control panel.

Create routing controls to manage traffic shifts between Availability Zones.

Use the routing control to shift traffic away from the affected Availability Zone.

This solution will meet the requirements because it will use a CloudFormation custom resource to execute custom logic during the stack set operation. A custom resource is a resource that you define in your template and that is associated with an AWS Lambda function. The Lambda function runs whenever the custom resource is created, updated, or deleted, and can perform any actions that are supported by the AWS SDK. In this case, the Lambda function can use the GuardDuty API to check whether GuardDuty is already enabled in each target account, and if not, enable it. This way, the DevOps engineer can avoid deploying the stack set to accounts that already have GuardDuty enabled, and prevent failure during the deployment.

In the source AWS account, the IAM role used by the CI/CD pipeline should have permissions to access the source code repository, build artifacts, and any other resources required for the build process. In the destination AWS accounts, the IAM role used for deployment should have permissions to access the AWS resources required for deploying the application, such as EC2 instances, RDS databases, S3 buckets, etc. The exact permissions required will depend on the specific resources being used by the application. the IAM role used for deployment in the destination accounts should also have permissions to assume the IAM role for deployment in the centralized DevOps account. This is typically done using an IAM role trust policy that allows the destination account to assume the DevOps account role.

To meet the new guideline for application deployment, the company can use a combination of AWS CodePipeline and AWS CloudTrail. A manual approval action in CodePipeline allows the security team to review and approve changes before they are deployed. This action can be configured to pause the pipeline until approval is granted, ensuring that no changes move to production without the necessary sign-off. Additionally, by creating an AWS CloudTrail trail, all actions taken within CodePipeline, including approvals, are recorded and delivered to an Amazon S3 bucket. This provides an audit trail that can be retained for compliance and review purposes.

AWS CodePipeline’s manual approval action provides a way to ensure that a member of the security team can review and approve changes before they are deployed1.

AWS CloudTrail integration with CodePipeline allows for the recording and retention of all pipeline actions, including approvals, which can be stored in Amazon S3 for record-keeping2.

To validate AWS CDK-defined infrastructure, use CDK Assertions (assertions module) to test synthesized CloudFormation templates (e.g., template.hasResourceProperties). Integrate testing into CodeBuild buildspec before deployment, setting OnFailure: ABORT so the pipeline halts if tests fail. This ensures safe continuous delivery following AWS CDK testing best practices.

A (S3 RTC) is required to meet the “replicated within 15 minutes” requirement in a verifiable way. Replication Time Control is the S3 feature that provides a 15-minute replication time SLA for replicated objects (instead of “best effort” replication timing).

B (S3 Multi-Region Access Point, active-passive) provides the most operationally efficient failover approach. An MRAP gives you a single global access point for S3 across Regions and supports controlled routing to the chosen bucket, avoiding custom DNS logic per application/client.

C (SubmitMultiRegionAccessPointRoutes) is the mechanism to perform failover for an MRAP by changing which Regional bucket is routed for the Multi-Region Access Point. This gives an explicit, API-driven “fail over now” action aligned with active-passive routing.

Why the others aren’t part of the “most operationally efficient” three:

D (Transfer Acceleration) optimizes upload/download performance over long distances , but it does not guarantee cross-Region replication within 15 minutes and doesn’t provide failover control.

E and F (Route 53 ARC) : ARC is great for controlling failover for endpoints behind routing controls, but for S3 bucket access the more direct and operationally simple approach is S3 Multi-Region Access Points with route updates via the S3 API. Using ARC here adds extra components/process compared to MRAP-native routing control.

ALBs with Auto Scaling across AZs ensure high availability in each Region.

Aurora global database supports cross-Region read replicas with low latency for reads and is recommended for multi-Region high availability.

Route 53 latency-based routing sends users to the closest Region.

RDS without global database (Options B, D) has higher replication lag and less resilience.

Using CloudFront in front of ALBs (Options C and D) adds caching but is not required for latency-based routing and increases complexity.