DOP-C02 Exam Dumps : AWS Certified DevOps Engineer - Professional

Amazon DevOps Guru is a fully managed service that uses machine learning to detect anomalous application behavior and provides insights with recommended remediation actions to improve operational performance.

Enabling DevOps Guru (Option A) covers many AWS resources automatically and reduces manual analysis.

Configuring SNS notifications (Option B) allows the team to be proactively alerted about important events.

Options C, D, and E require manual data setup, complex querying, and dashboard/reporting maintenance, which increase operational overhead. Therefore, the combination of enabling DevOps Guru and setting up notifications achieves the goals with minimal effort.

The condition is deterministic: maximum CPU utilization must be below 2% for the previous 24 one-hour periods. That requires a static threshold, not anomaly detection. Anomaly detection learns a normal band from historical behavior, which is useful for unusual patterns but not for a fixed business definition of “idle.” CloudWatch alarms use “Datapoints to Alarm” as an M-out-of-N evaluation model, so 24 out of 24 means all 24 hourly datapoints must breach the threshold. AWS also documents idle-instance alarm examples based on CPU utilization staying below a threshold for 24 consecutive periods. Option D would alarm if only one hour was below 2%, which is far too sensitive. Options A and C use the wrong threshold type.

================

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