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

To enable cross-account backup, the company needs to grant permissions to both the backup vault and the KMS key in the destination account. The backup vault access policy in the destination account must allow the primary account to copy backups into the vault. The key policy of the KMS key in the destination account must allow the primary account to use the key to encrypt and decrypt the backups. These steps are described in the AWS documentation12. Therefore, the correct answer is A and E.

1: Creating backup copies across AWS accounts - AWS Backup

2: Using AWS Backup with AWS Organizations - AWS Backup

The question focuses on detecting supply chain attacks that bypass CI/CD controls and result in malicious runtime behavior in the deployed serverless application. This requires runtime threat detection , not just static scanning or infrastructure validation.

Amazon GuardDuty is AWS’s managed threat detection service. GuardDuty now includes Lambda Protection , which analyzes Lambda function behavior, including network calls, IAM API usage, and other signals to detect compromised functions, crypto-mining, data exfiltration, and other malicious activity. GuardDuty automatically ingests CloudTrail, VPC Flow Logs (if enabled), and other telemetry without requiring code changes or agents. Findings are published and can be routed to Amazon EventBridge for near real-time notifications or automated remediation.

Option A (AWS WAF) only inspects HTTP requests to API Gateway and is focused on known bad patterns, not deeper behavioral anomalies. Option C (CloudFormation Guard) is focused on infrastructure-as-code validation in the pipeline, which explicitly does not help once malicious code is deployed. Option D (Network Firewall with Emerging Threats rules) protects VPC traffic, but serverless components like Lambda and API Gateway may not all traverse that firewall, and it is not targeted at function-level behavior.

Hence, enabling GuardDuty with Lambda Protection and using EventBridge notifications is the correct solution.

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