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

This is a classic AWS Organizations governance design:

Restrict Regions and allowed services across all accounts

The correct Organizations mechanism for account-wide guardrails is a Service Control Policy (SCP) .

SCPs set the maximum permissions that accounts/OUs can use, making them the right tool to enforce “only these Regions” and “only these services” consistently across the org.

Authentication from Active Directory + consistent job-function roles in every account

With AD as the identity source, the standard approach is federation to AWS using an IAM identity provider (SAML) and role assumption based on job function.

AWS CloudFormation StackSets is the operationally efficient way to deploy identical IAM roles/policies into multiple accounts/OUs so job-function permissions are consistent everywhere.

The roles’ trust policies can be configured to allow federated identities (from the AD-backed IdP) to assume the correct job role in each account.

Why the other options don’t fit:

A “OU with group policies” isn’t an AWS Organizations control mechanism for restricting Regions/services. The AWS-native control for this is SCPs , not “group policies.”

B Permissions boundaries are useful to limit what an IAM principal/role can do within an account , but they are not the org-wide enforcement mechanism for “all accounts must stay in these Regions / use only these services.” That’s what SCPs do.

C AWS RAM is for sharing resources (like subnets, Transit Gateway, etc.), not for “sharing roles” across accounts as a governance baseline. Also, the question emphasizes identical permissions in each account, which is best achieved by provisioning roles in each account (StackSets), not attempting to “share” 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

Service-managed StackSets are the correct low-overhead deployment model when AWS Organizations trusted access is enabled. CloudFormation creates and manages the required StackSet execution roles, so self-managed permissions are unnecessary. However, AWS explicitly states that service-managed StackSets do not deploy stacks to the organization management account, even if the management account is part of the organization or belongs to an OU. Therefore, targeting the root OU deploys to the member accounts, but the management account still needs a separate normal CloudFormation stack. Option B is the only answer that covers both the member accounts and the management account with the least operational overhead. Option A misses the management account, while C and D require unnecessary self-managed role administration.

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