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

Option B is correct because the requirement is to retain a very large dataset indefinitely and analyze it from Amazon Redshift in the most cost-effective way. Amazon Redshift Spectrum allows Redshift to query data directly in Amazon S3 without loading all of the data into Redshift-managed storage. That reduces warehouse storage cost for long-term retained data. AWS documentation also recommends using Apache Parquet for Spectrum because Parquet is a columnar format, which allows Redshift Spectrum to read only the columns needed instead of scanning entire text files. This improves performance and lowers query cost.

Option A and D are less cost-effective because auto-copy loads data into the Redshift cluster, which means the company pays to store all historical data in Redshift even though the requirement is indefinite retention of a large volume of IoT data. Option C is worse than B because JSON is a row-oriented text format, and AWS guidance says columnar formats such as Parquet or ORC are preferred for Redshift Spectrum for better scan efficiency and lower cost.

Option A is correct. The MERGE INTO statement is used to conditionally update, insert, or delete rows based on a match condition between a target table and a source table. In this statement, the target table is accounts and the source table is monthly_accounts_update. The join condition is t.customer = s.customer. Because the statement uses WHEN MATCHED THEN DELETE, every row in accounts that has a matching customer value in monthly_accounts_update will be deleted from the target table.

AWS documentation for MERGE INTO states that it conditionally updates, deletes, or inserts rows into an Apache Iceberg table, and the syntax explicitly includes WHEN MATCHED THEN DELETE. AWS Glue guidance and examples for Iceberg also show MERGE INTO as a supported pattern for row-level changes in Glue ETL workflows. This means the syntax is valid for supported Glue-Iceberg use cases, so option D is incorrect. Options B and C are also incorrect because the statement does not retain only matching rows and does not delete the entire table. It deletes only the rows in the target table that satisfy the match condition.

Scheduling an AWS Glue crawler to periodically update the Data Catalog automates the process of detecting new partitions and updating the catalog, which minimizes manual maintenance and operational overhead.