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Option C best matches the requirement of no new code with least operational effort because it keeps the analyst’s work inside the AWS-native, visual ETL experience and produces standardized outputs that data engineers can extend. The study material emphasizes that AWS provides visual data preparation capabilities that let users “clean and normalize data without writing any code,” which is exactly what the analyst needs before advanced engineering transformations begin.

Option A requires Python and Pandas, which directly violates the “does not require writing new code” requirement and introduces dependency management and debugging overhead. Option B uses multiple services (Canvas + Data Wrangler + Glue), which increases the number of moving parts, permissions, handoffs, and operational surface area compared to a single-service preparation approach. Option D offloads the entire preparation step to engineers, which increases operational effort and delays because the analyst cannot directly implement and iterate on standardization.

Using recipe-style transformations in the Glue visual interface aligns with the documented goal of simplifying data preparation workflows while enabling the engineering team to add more complex steps later in the pipeline.

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]

In Amazon Redshift, the SUPER data type is designed specifically to handle semi-structured data like JSON, Parquet, ORC, and others. By using the SUPER data type, Redshift can ingest and query JSON data without requiring complex data flattening processes, thus reducing the amount of preprocessing required before loading the data. The SUPER data type also works seamlessly with Redshift Spectrum, enabling complex queries that can combine both structured and semi-structured datasets, which aligns with the company’s need to use combined datasets to identify potential new customers.

Using the SUPER data type also allows for automatic parsing and query processing of nested data structures through Amazon Redshift ' s PARTITION BY and JSONPATH expressions, which makes this option the most efficient approach with the least effort involved. This reduces the overhead associated with using tools like AWS Glue or Lambda for data transformation.

Amazon Redshift Documentation - SUPER Data Type

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AWS Certified Data Engineer - Associate Study Guide

By directly leveraging the capabilities of Redshift with the SUPER data type, the data engineer ensures streamlined JSON ingestion with minimal effort while maintaining query efficiency.