AWS Certified Data Engineer Associate DEA-C01 Practice Question
An e-commerce company loads 3 TB of new transactional data into its data warehouse on Amazon S3 every day. A 4-node Amazon Redshift RA3 cluster currently satisfies most analytic queries, but at month-end up to 400 concurrent connections cause queueing and long runtimes. The company wants to maintain current cost for daily operations and pay only for the occasional spikes. Which solution meets these requirements with minimal administration?
Enable Concurrency Scaling on the existing Amazon Redshift RA3 cluster to add transient compute during high-concurrency periods.
Resize the cluster to 16 RA3 nodes so it can handle the month-end workload without queueing.
Migrate the workload to Amazon Redshift Serverless and set the base capacity to support 400 concurrent queries.
Move the data warehouse to Amazon RDS for PostgreSQL and configure Auto Scaling read replicas for additional capacity.
Enabling Concurrency Scaling on the existing Amazon Redshift cluster keeps the 4-node RA3 footprint for the daily workload and automatically launches transient clusters only when the query queue grows. The first hour of Concurrency Scaling per cluster per day is free, and additional usage is billed per-second, so the company pays only during the month-end spike without manual resizing. Expanding the cluster to 16 nodes would incur higher fixed costs all month. Configuring Redshift Serverless for peak capacity would also drive up compute charges during heavy use and requires re-architecting the deployment. Amazon RDS with Auto Scaling read replicas does not provide the massively parallel processing engine needed for large analytic joins and would still suffer under the concurrency spike.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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