Amazon Web Services DOP-C02 Exam With Confidence Using Practice Dumps

To allow only creation/configuration of service-linked roles , you need a way to tightly scope what IAM actions the developer can perform. The most AWS-appropriate mechanism for “delegate limited IAM admin” is a role with a permissions boundary :

A permissions boundary sets the maximum permissions the role (and any roles it creates, depending on design) can ever have. This is a standard pattern to safely delegate IAM tasks without granting broad IAM administration.

You can define the role’s policy + boundary so the developer can call only the APIs required for service-linked roles (for example actions like creating service-linked roles and passing only allowed AWS service principals), while preventing general role/policy creation outside that scope.

Why the others don’t meet “service-linked roles only”:

A is vague (“common services”) and cross-account access doesn’t inherently restrict to only service-linked roles. It’s also more operational overhead than needed for a single dev account use case.

B PowerUserAccess is far too broad and does not restrict to service-linked roles.

C An SCP with Deny iam:* would block IAM entirely (including what the developer needs). Even if refined, SCPs set account-wide guardrails and are not the right tool to grant a single developer the precise ability to create only service-linked roles.

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