AWS Certified Data Engineer Associate DEA-C01 Practice Question
A data engineering team uses an Amazon Redshift cluster as its enterprise data warehouse. Each night they must execute a series of DELETE, INSERT, and UPDATE statements that include conditional branching and loop logic to consolidate staging data into fact tables. The team wants to keep all processing inside the cluster and trigger the workflow with a single SQL call. Which Amazon Redshift feature meets these requirements?
Develop an AWS Glue ETL job that connects to the cluster and runs the SQL script from an external Python shell.
Schedule an automatic materialized view refresh that encapsulates the required transformations.
Configure Amazon EventBridge to run each SQL statement through the Amazon Redshift Data API on a fixed schedule.
Create a stored procedure in Amazon Redshift using PL/pgSQL and invoke it with a CALL statement each night.
Amazon Redshift stored procedures are written in PL/pgSQL and run inside the cluster. They support control-of-flow constructs such as IF statements, loops, and exception handlers, allowing multi-step data manipulation to be packaged as a single routine that can be invoked with a CALL statement. Materialized view refreshes can only run a SELECT query, views are read-only, and COPY or Data API schedules would require multiple external calls or compute outside Redshift. Therefore, a stored procedure best satisfies the need for in-database scripting with a single entry point.
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What is a stored procedure in Amazon Redshift?
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What is the difference between a stored procedure and a materialized view in Amazon Redshift?
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How does Amazon Redshift's PL/pgSQL differ from standard SQL?
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What is a stored procedure in Amazon Redshift?
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What is PL/pgSQL and how is it used in Amazon Redshift?
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Why are materialized views not suitable for this use case?
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AWS Certified Data Engineer Associate DEA-C01
Data Operations and Support
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