AWS Certified Data Engineer Associate DEA-C01 Practice Question
A company has an Amazon Redshift cluster in us-east-1 and uses Redshift Spectrum to query external tables in an S3 data lake in the same Region. A new partitioned Parquet dataset arrives daily in an S3 bucket in eu-west-1, and analysts must join this data with existing Redshift tables. To meet this requirement, keep the data in S3, and minimize storage cost, which approach should the data engineer take?
Enable S3 cross-region replication to copy the Parquet files to a bucket in us-east-1, then point the external table definition at the replicated data.
Use Amazon Redshift federated queries to read the Parquet files directly from the eu-west-1 bucket and join them with local tables.
Add a VPC endpoint for eu-west-1 S3 to the us-east-1 VPC so Redshift Spectrum can read the remote bucket without moving the data.
Create a new external schema that references the eu-west-1 bucket and include REGION 'eu-west-1' in the LOCATION clause of each external table.
Redshift Spectrum can query only external tables whose data files reside in the same AWS Region as the Amazon Redshift cluster. Because the cluster is in us-east-1, the Parquet files in eu-west-1 are not directly accessible. Enabling S3 cross-region replication to copy the objects to a bucket in us-east-1 keeps the data in S3, avoids relocating it into Redshift, and removes the Region limitation so the engineer can create or update external tables that reference the replicated prefix. The other options rely on features that Redshift Spectrum does not support: specifying a remote Region in the LOCATION clause, using VPC endpoints to bypass the Region rule, or employing Redshift federated queries, which work with relational databases rather than S3 objects.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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