Most Recent Amazon DOP-C02 Exam Dumps

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

* Use AWS Step Functions to Orchestrate Processing:

AWS Step Functions allow you to build distributed applications by combining AWS Lambda functions or other AWS services into workflows.

Decoupling the processing into Step Functions tasks enables you to retry individual steps without reprocessing the entire record.

* Architectural Steps:

Create a web application to pass records to AWS Step Functions:

The web application can be a simple frontend that receives input and triggers the Step Functions workflow.

Define a Step Functions state machine:

Each step in the state machine represents a processing stage. If a step fails, Step Functions can retry the step based on defined conditions.

Use AWS Lambda functions:

Lambda functions can be used to handle each processing step. These functions can be stateless and handle specific tasks, reducing the complexity of error handling and reprocessing logic.

* Operational Efficiency:

Using Step Functions and Lambda improves operational efficiency by providing built-in error handling, retries, and state management.

This architecture scales automatically and isolates failures to individual steps, ensuring only failed steps are retried.

AWS Step Functions

Building Workflows with Step Functions

* 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.

Disabling cross-zone load balancing

Route 53 Application Recovery Controller

The correct answer is C, Allowing users to deploy CloudFormation stacks using AWS Service Catalog only and enforcing the use of a launch constraint is the best way to ensure that the internal business teams launch resources through pre-approved CloudFormation templates only. AWS Service Catalog is a service that enables organizations to create and manage catalogs of IT services that are approved for use on AWS. A launch constraint is a rule that specifies the role that AWS Service Catalog assumes when launching a product. By using a launch constraint, the DevOps engineer can control the permissions that the users have when launching a product. Using AWS Config rules to detect when resources have drifted from their expected state is the best way to automate the monitoring of the resources. AWS Config is a service that enables you to assess, audit, and evaluate the configurations of your AWS resources. AWS Config rules are custom or managed rules that AWS Config uses to evaluate whether your AWS resources comply with your desired configurations. By using AWS Config rules, the DevOps engineer can track the changes in the resources and identify any non-compliant resources.

Option A is incorrect because allowing users to deploy CloudFormation stacks using a CloudFormation service role only is not the best way to ensure that the internal business teams launch resources through pre-approved CloudFormation templates only. A CloudFormation service role is an IAM role that CloudFormation assumes to create, update, or delete the stack resources. By using a CloudFormation service role, the DevOps engineer can control the permissions that CloudFormation has when acting on the resources, but not the permissions that the users have when launching a stack. Therefore, option A does not prevent the users from launching resources that are not approved by the company. Using CloudFormation drift detection to detect when resources have drifted from their expected state is a valid way to monitor the resources, but it is not as automated and scalable as using AWS Config rules. CloudFormation drift detection is a feature that enables you to detect whether a stack's actual configuration differs, or has drifted, from its expected configuration. To use this feature, the DevOps engineer would need to manually initiate a drift detection operation on the stack or the stack resources, and then view the drift status and details in the CloudFormation console or API.

Option B is incorrect because allowing users to deploy CloudFormation stacks using a CloudFormation service role only is not the best way to ensure that the internal business teams launch resources through pre-approved CloudFormation templates only, as explained in option A. Using AWS Config rules to detect when resources have drifted from their expected state is a valid way to monitor the resources, as explained in option C,

Option D is incorrect because enforcing the use of a template constraint is not the best way to ensure that the internal business teams launch resources through pre-approved CloudFormation templates only. A template constraint is a rule that defines the values or properties that users can specify when launching a product. By using a template constraint, the DevOps engineer can control the parameters that the users can provide when launching a product, but not the permissions that the users have when launching a product. Therefore, option D does not prevent the users from launching resources that are not approved by the company. Using Amazon EventBridge notifications to detect when resources have drifted from their expected state is a less reliable and consistent solution than using AWS Config rules. Amazon EventBridge is a service that enables you to connect your applications with data from a variety of sources. Amazon EventBridge can deliver a stream of real-time data from event sources, such as AWS services, and route that data to targets, such as AWS Lambda functions. However, to use this solution, the DevOps engineer would need to configure the event source, the event bus, the event rule, and the event target for each resource type that needs to be monitored, which is more complex and error-prone than using AWS Config rules.

Step 2: Use an Application Load Balancer and Auto Scaling Group The Application Load Balancer (ALB) is essential to balance traffic across multiple instances, and Auto Scaling ensures the deployment scales automatically to meet demand.

Action: Associate the Auto Scaling group and Application Load Balancer target group with the deployment group.

Why: This configuration ensures that traffic is evenly distributed and that instances automatically scale based on traffic load.

Step 3: Use Custom Deployment Configuration The company requires that traffic be rerouted to at least half of the instances to succeed. AWS CodeDeploy allows you to configure custom deployment settings with specific thresholds for healthy hosts.

Action: Create a custom deployment configuration where 50% of the instances must be healthy.

Why: This ensures that the deployment continues only if at least 50% of the new instances are healthy.

Step 4: Clean Temporary Files Using Hooks Before routing traffic to the new environment, the temporary files generated during the deployment must be deleted. This can be achieved using the BeforeAllowTraffic hook in the appspec.yml file.

Action: Use the BeforeAllowTraffic lifecycle event hook to clean up temporary files before routing traffic to the new environment.

Why: This ensures that the environment is clean before the new instances start serving traffic.

Step 5: Terminate Original Instances After Deployment After a successful deployment, AWS CodeDeploy can automatically terminate the original instances (blue environment) to save costs.

Action: Instruct AWS CodeDeploy to terminate the original instances after the new instances are healthy.

Why: This helps in cost reduction by removing unused instances after the deployment.

This corresponds to Option C: Use an Application Load Balancer and a blue/green deployment. Associate the Auto Scaling group and the Application Load Balancer target group with the deployment group. Use the Automatically copy Auto Scaling group option, and use CodeDeployDefault.HalfAtATime as the deployment configuration. Instruct AWS CodeDeploy to terminate the original instances in the deployment group, and use the BeforeAllowTraffic hook within appspec.yml to delete the temporary files.