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

The company wants to gather information about incomplete multipart uploads and outdated versions in its Amazon S3 buckets to optimize storage costs.

Option B: Use Amazon S3 Inventory configurations reports to gather the information.S3 Inventory provides reports that can list incomplete multipart uploads and versions of objects stored in S3. It offers an easy, automated way to track object metadata across buckets, including data necessary for cost optimization, without manual effort.

Options A (AWS CLI), C (S3 Storage Lens), and D (usage reports) either do not specifically gather the required information about incomplete uploads and outdated versions or require more manual intervention.

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

AWS Certified Data Engineer - Associate Training: Building Batch Data Analytics Solutions on AWS

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.

AWS Glue DataBrew provides a no-code way to define and run data quality rulesets for data stored in S3. You can trigger profiling jobs via Amazon EventBridge on new uploads for automated checks.

“Use AWS Glue DataBrew to define and run data quality rules on S3 datasets with minimal coding effort. Automate validation by triggering jobs through EventBridge.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf