Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

Option A is the most direct and operationally efficient way to trigger the existing Lambda-based load into Amazon Redshift whenever a new CSV file is uploaded to Amazon S3. The key requirement is event-driven automation on “new object created” events, and the study material explicitly describes using Amazon EventBridge to react to S3 uploads by creating a rule on the default event bus and routing matching events to a target application.

Compared with queue-based designs (Options B and D), Option A reduces components: there is no need to manage an SQS queue, batching/visibility timeout behavior, or retry semantics at the consumer layer. This is especially appropriate because the files are small (≤ 5 MB) and the Lambda function is already implemented to perform the copy/load step. Using DMS (Option C) is not designed for “S3 object arrival → Lambda → Redshift” event triggering; it introduces unnecessary services and operational work for a simple file-ingestion trigger.

Therefore, integrating S3 event notifications with EventBridge and targeting the Lambda function best meets the requirement with the least added complexity.

AWS Step Functions is a fully managed, serverless orchestration service designed to coordinate multiple AWS services into reliable workflows. Step Functions natively provide automatic retries, error handling, state-level visibility, and execution history, which directly satisfy the requirements for retry logic and monitoring individual pipeline stages.

In this solution, AWS Lambda functions are used for batch data ingestion, AWS Glue jobs handle transformation, and Amazon S3 is used for storage. Step Functions orchestrate these stages while maintaining clear separation of responsibilities. Each step can be configured with retry and catch policies, allowing the workflow to automatically recover from transient failures without manual intervention.

Chaining AWS Glue jobs alone does not provide detailed stage-level visibility or flexible retry control, and setting MaxRetries to 0 contradicts the retry requirement. EventBridge-based pipelines lack native workflow state tracking. Amazon MWAA introduces unnecessary operational overhead, including environment management and Airflow maintenance.

Therefore, using AWS Step Functions to orchestrate Lambda and Glue provides the most scalable, observable, and low-maintenance solution.

AWS Lake Formation is a service that helps you build, secure, and manage data lakes on Amazon S3. You can use AWS Lake Formation to register the S3 path as a data lake location, and enable fine-grained access control to limit access to the records based on the HR department’s Region. You can use data filters to specify which S3 prefixes or partitions each HR department can access, and grant permissions to the IAM roles of the HR departments accordingly. This solution will meet the requirement with the least operational overhead, as it simplifies the data lake management and security, and leverages the existing IAM roles of the HR departments12.

The other options are not optimal for the following reasons:

A. Use data filters for each Region to register the S3 paths as data locations. This option is not possible, as data filters are not used to register S3 paths as data locations, but to grant permissions to access specific S3 prefixes or partitions within a data location. Moreover, this option does not specify how to limit access to the records based on the HR department’s Region.

C. Modify the IAM roles of the HR departments to add a data filter for each department’s Region. This option is not possible, as data filters are not added to IAM roles, but to permissions granted by AWS Lake Formation. Moreover, this option does not specify how to register the S3 path as a data lake location, or how to enable fine-grained access control in AWS Lake Formation.

E. Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region. This option is not recommended, as it would require more operational overhead to create and manage multiple S3 buckets, and to configure and maintain IAM policies for each HR department. Moreover, this option does not leverage the benefits of AWS Lake Formation, such as data cataloging, data transformation, and data governance.

1: AWS Lake Formation

2: AWS Lake Formation Permissions

AWS Identity and Access Management

Amazon S3