Which combination of steps should the developer take to meet these requirements?
(Choose two.)
Stream the CloudFront distribution logs to an Amazon S3 bucket. Detect anomalies and error rates by using Amazon Athena.
Enable real-time logs on the CloudFront distribution. Create a data stream in Amazon Kinesis Data Streams.
Set up Amazon Kinesis Data Streams to send the logs to Amazon OpenSearch Service by using an AWS Lambda function. Make a dashboard in OpenSearch Dashboards.
Stream the CloudFront distribution logs to Amazon Kinesis Data Firehose.
Set up Amazon Kinesis Data Firehose to send the logs to AWS CloudTrail. Create CloudTrail metrics, alarms, and dashboards.
Explanations:
Streaming CloudFront logs to S3 and using Amazon Athena is a valid approach for analyzing data, but it is not the most efficient for real-time monitoring of error rates and anomalies since Athena does not provide real-time capabilities.
Enabling real-time logs on CloudFront provides immediate access to log data, which is essential for monitoring error rates and anomalies. Creating a data stream in Amazon Kinesis Data Streams allows for real-time data processing.
Setting up Kinesis Data Streams to send logs to Amazon OpenSearch Service is a suitable solution for real-time monitoring and visualization. OpenSearch Dashboards can create a dashboard that reflects the current status and anomalies in error rates.
Streaming CloudFront logs to Amazon Kinesis Data Firehose is a good option for processing logs but does not directly address the need for real-time monitoring and visualization. It is also a one-step solution rather than a complete monitoring solution.
Sending logs to AWS CloudTrail is not applicable since CloudTrail is primarily for tracking API calls in AWS, not for monitoring application logs or performance metrics. This option does not address the requirements for monitoring CloudFront error rates.