Amazon Web Services DOP-C02 Exam With Confidence Using Practice 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

Comprehensive and Detailed Explanation From Exact Extract:

For gradual traffic shifting in Lambda deployments, AWS Lambda aliases support the RoutingConfig property, which specifies the percentage of traffic routed to different versions of the Lambda function. This enables weighted traffic shifting between versions as part of deployment strategies.

The AWS::Lambda::Alias resource’s RoutingConfig can specify multiple versions with weights, enabling canary or linear deployment strategies without needing CodeDeploy resources explicitly.

AWS CodeDeploy resources like DeploymentConfig and DeploymentGroup are used primarily for blue/green deployments and managing deployment strategies outside Lambda aliases.

Lambda versions themselves do not have a VersionWeight property; instead, weighted routing is managed via aliases.

A Lambda function in one account can mount a file system in a different account. For this scenario, you configure VPC peering between the function VPC and the file system VPC.

1. Need to update the file system policy on EFS to allow mounting the file system into Account B.

## File System Policy

$ cat file-system-policy.json

{

"Statement": [

{

"Effect": "Allow",

"Action": [

"elasticfilesystem:ClientMount",

"elasticfilesystem:ClientWrite"

],

"Principal": {

"AWS": "arn:aws:iam:::root" # Replace with AWS account ID of EKS cluster

}

}

]

}

2. Need VPC peering between Account A and Account B as the pre-requisite

3. Need to assume cross-account IAM role to describe the mounts so that a specific mount can be chosen.