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

The analytics team wants to copy only a subset of the data, run the copy as soon as data is ready, and receive notifications when the process completes. AWS DataSync supports the use of manifest files to control exactly which files or objects are transferred. By generating a manifest file on premises that lists only the relevant data and uploading that manifest to Amazon S3, the company can precisely limit what DataSync copies, which reduces data transfer costs and processing overhead.

Option A satisfies the requirement to identify only analytics-relevant data by creating and uploading a manifest file that lists the objects to be transferred. This avoids copying unnecessary data and is cost-effective.

Option D uses Amazon S3 Event Notifications to trigger an AWS Lambda function as soon as the manifest file is uploaded. The Lambda function starts the DataSync task and references the manifest file. This ensures that the copy process begins immediately after data generation, rather than waiting for a fixed schedule, which meets the “as soon as possible” requirement.

Option E provides a fully managed and scalable notification mechanism. AWS DataSync publishes task execution state changes as events. Amazon EventBridge can capture SUCCESS or ERROR states and route those events to Amazon SNS, which can then send email notifications. This approach avoids custom polling logic and minimizes operational overhead.

Option B and C introduce Amazon DynamoDB unnecessarily. DataSync can directly consume a manifest file from Amazon S3, so loading manifest data into DynamoDB and orchestrating copy logic manually adds cost and complexity without benefit.

Option F relies on a custom Lambda function to monitor task state changes, which increases operational overhead compared to the native EventBridge integration with DataSync.

Therefore, using S3-based manifests, event-driven invocation of DataSync, and EventBridge-based notifications is the most cost-effective and operationally efficient solution.

The best solution is to deploy an AWS Lambda function to add capacity to the Amazon Redshift cluster by using an elastic resize operation when the cluster’s CPU metrics in Amazon CloudWatch reach 80%. This solution will enable the cluster to scale up or down quickly by adding or removing nodes within minutes. This will improve the performance of the complex read queries and also reduce the cost by scaling down when the demand decreases. This solution is more cost-effective than using a classic resize operation, which takes longer and requires more downtime. It is also more suitable than using Amazon EMR, which is designed for big data processing rather than data warehousing. References: Amazon Redshift Documentation, Resizing clusters in Amazon Redshift, [Amazon EMR Documentation]