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

Option B is most cost-effective because it combines a serverless ETL service for overnight processing with a serverless warehouse for partner SQL access during business hours. The material describes AWS Glue as a “fully managed, serverless, scalable ETL service” that can extract, transform, and load data with minimal operational overhead, which fits the requirement for multiple transformations and data cleaning.

For the SQL access layer, the study material highlights the cost advantage of Amazon Redshift Serverless: it automatically provisions and scales capacity and you pay only for the compute capacity provisioned, with no compute costs when no workloads are running. That directly supports “multiple business partners” querying during business hours while avoiding the always-on cost of a provisioned cluster.

Options C and D use a provisioned Redshift cluster, which is less cost-effective because capacity must be paid for even when partners are not querying. Option A adds EMR cluster management cost and operational overhead compared to using Glue for ETL.

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.

The correct answer is to configure AWS Glue triggers to run the ETL jobs every hour and use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift. AWS Glue triggers are a way to schedule and orchestrate ETL jobs with the least operational overhead. AWS Glue connections are a way to securely connect to data sources and targets using JDBC or MongoDB drivers. AWS Glue DataBrew is a visual data preparation tool that does not support MongoDB as a data source. AWS Lambda functions are a serverless option to schedule and run ETL jobs, but they have a limit of 15 minutes for execution time, which may not be enough for complex transformations. The Redshift Data API is a way to run SQL commands on Amazon Redshift clusters without needing a persistent connection, but it does not support loading data from AWS Glue ETL jobs. References:

AWS Glue triggers

AWS Glue connections

AWS Glue DataBrew

[AWS Lambda functions]

[Redshift Data API]