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

 The Amazon Redshift Data API is a built-in feature that allows you to run SQL queries on Amazon Redshift data with web services-based applications, such as AWS Lambda, Amazon SageMaker notebooks, and AWS Cloud9. The Data API does not require a persistent connection to your database, and it provides a secure HTTP endpoint and integration with AWS SDKs. You can use the endpoint to run SQL statements without managing connections. The Data API also supports both Amazon Redshift provisioned clusters and Redshift Serverless workgroups. The Data API is the best solution for running real-time queries on the financial data from within the trading application, as it has the least operational overhead compared to the other options.

Option A is not the best solution, as establishing WebSocket connections to Amazon Redshift would require more configuration and maintenance than using the Data API. WebSocket connections are also not supported by Amazon Redshift clusters or serverless workgroups.

Option C is not the best solution, as setting up JDBC connections to Amazon Redshift would also require more configuration and maintenance than using the Data API. JDBC connections are also not supported by Redshift Serverless workgroups.

Option D is not the best solution, as storing frequently accessed data in Amazon S3 and using Amazon S3 Select to run the queries would introduce additional latency and complexity than using the Data API. Amazon S3 Select is also not optimized for real-time queries, as it scans the entire object before returning the results. References:

Using the Amazon Redshift Data API

Calling the Data API

Amazon Redshift Data API Reference

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

To achieve the highest throughput and efficiently use cluster resources while loading data into an Amazon Redshift cluster, the optimal approach is to use a single COPY command that ingests data in parallel.

Option D: Use a single COPY command to load the data into the Redshift cluster.The COPY command is designed to load data from multiple files in parallel into a Redshift table, using all the cluster nodes to optimize the load process. Redshift is optimized for parallel processing, and a single COPY command can load multiple files at once, maximizing throughput.

Options A, B, and C either involve unnecessary complexity or inefficient approaches, such as using multiple COPY commands or INSERT statements, which are not optimized for bulk loading.

Amazon Aurora zero-ETL integration with Amazon Redshift Serverless is designed specifically to provide near real-time analytics on transactional data with minimal operational overhead. This integration continuously replicates changes from Aurora MySQL databases into Redshift Serverless without requiring data pipelines, replication tasks, or infrastructure management.

Because the requirement emphasizes near real-time latency and minimal management, zero-ETL integration is the optimal choice. It eliminates the need to configure and monitor AWS DMS tasks, manage Glue jobs, or build and scale Lambda-based ingestion logic. Schema changes, data synchronization, and replication are handled automatically by AWS.

AWS DMS Serverless, while managed, still requires task configuration and monitoring and typically introduces more latency than zero-ETL integrations. AWS Glue jobs and Lambda functions require custom code, scheduling, and error handling, increasing both complexity and maintenance effort.

Therefore, setting up Aurora zero-ETL integration with Amazon Redshift Serverless best satisfies the requirements and aligns with AWS best practices for modern analytics architectures.