A VPC with predictable traffic has VPC flow logs sent to a CloudWatch Logs log group. The team needs a monitoring solution that detects anomalies in VPC traffic patterns over time and can trigger a response when an anomaly is detected. How should they implement this?

The correct solution leverages Amazon Lookout for Metrics, a fully managed service specifically designed for anomaly detection in time-series data, making it ideal for VPC flow logs. Kinesis Data Firehose efficiently streams the logs to S3, which is a supported data source for Lookout for Metrics. When Lookout for Metrics detects an anomaly, it can trigger a Lambda function to publish an event to EventBridge, enabling further automated responses. Incorrect options: Creating a Kinesis data stream and using Kinesis Data Analytics for anomaly detection is possible but more complex to set up and manage for this specific use case compared to Lookout for Metrics, which is purpose-built for anomaly detection. Writing a custom Lambda function for anomaly detection is less efficient and scalable than using a managed service like Lookout for Metrics, which handles the underlying machine learning models. Using a Kinesis data stream with a Lambda processor for anomaly detection is also a custom solution that requires more development and maintenance effort than leveraging Lookout for Metrics.