AWS Certified Data Engineer Associate DEA-C01 Practice Question
A company uses the COPY command to load raw IoT JSON files from Amazon S3 into an Amazon Redshift table. New firmware versions frequently add optional attributes. Analysts must query new attributes immediately without running ALTER TABLE for each change. Which Redshift schema-evolution technique meets this requirement while keeping existing queries unchanged?
Run an AWS Glue crawler after each firmware release to detect new attributes and automatically add columns to the Redshift table.
Use the COPY command with an AUTOEVOLVE parameter to create any missing columns during each load.
Load the JSON into a column defined as SUPER and use PartiQL to query any new keys without altering the table.
Convert the JSON files to Parquet, create a new Redshift table with the updated schema each month, and UNION ALL the results in a view.
Storing the entire JSON payload in a column of type SUPER lets Amazon Redshift automatically accommodate any new attributes that arrive in future files. The COPY command can ingest the JSON into the SUPER column without prior knowledge of every key. Analysts can then use PartiQL syntax (for example, SELECT payload.newField) to read the newly added attributes immediately. No DDL is required when the schema changes, so historical queries that reference existing columns continue to work.
The COPY AUTOEVOLVE option does not exist, so it cannot add columns automatically. A Glue crawler can update external table metadata but cannot alter an internal Redshift table, and it still requires the data engineer to adjust the schema. Creating a new table each month and unioning the results adds maintenance overhead and query complexity without solving the need for seamless evolution.
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What is the SUPER data type in Amazon Redshift?
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How does PartiQL work with semi-structured data in Redshift?
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Why is AUTOEVOLVE not applicable in this scenario?
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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