The correct answer is Use an Amazon CloudWatch metric stream to send the EC2 Auto Scaling status data to Amazon Kinesis Data Firehose. Store the data in Amazon S3.

This option provides a serverless, real-time solution that does not affect EC2 instance performance. CloudWatch metric streams allow near-real-time delivery of metrics to destinations such as Kinesis Data Firehose, which can then buffer and batch data before delivering it to Amazon S3. This pattern supports low-latency ingestion and is well-suited for powering dashboards or analytics systems with minimal operational overhead.

Launch an Amazon EMR cluster to collect the EC2 Auto Scaling status data and send the data to Amazon Kinesis Data Firehose. Store the data in Amazon S3 is incorrect because EMR is not a serverless service and would introduce unnecessary complexity and cost. It's also not suitable for lightweight event tracking and would slow down the architecture.

Create an Amazon EventBridge rule to invoke an AWS Lambda function on a schedule. Configure the Lambda function to send the EC2 Auto Scaling status data directly to Amazon S3 is incorrect because scheduled invocations introduce latency and may miss real-time updates. EventBridge rules should be event-driven rather than scheduled for timely processing.

Use a bootstrap script during the launch of an EC2 instance to install Amazon Kinesis Agent. Configure Kinesis Agent to collect the EC2 Auto Scaling status data and send the data to Amazon Kinesis Data Firehose. Store the data in Amazon S3 is incorrect because it adds overhead to instance launch time, violating the requirement that the solution not impact launch speed. This approach also is not serverless.

Why this question matters: It tests your ability to use serverless event and metric pipelines that maintain real-time visibility while ensuring application scalability and performance are unaffected. Understanding how to connect monitoring data to storage and analytics systems is key in modern cloud architectures.

Tip: CloudWatch metric streams with Kinesis Data Firehose provide a near-real-time pipeline for metrics ingestion with minimal configuration and are ideal when the requirement includes low latency and a serverless architecture.

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