DAA-C01 Exam Dumps | A financial services company is migrating its transaction data from various sources, including mainframe

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Snowflake.DAA-C01.v2025-12-12.q151
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Question 131/151

A financial services company is migrating its transaction data from various sources, including mainframe systems, REST APIs, and flat files, into Snowflake. They want to leverage Snowflake's features for continuous data ingestion and transformation. Which combination of Snowflake features and external tools would provide the MOST scalable and efficient solution for this scenario?

A. Snowpipe with REST API integration using AWS Lambda functions triggering file loads from S3, and a Snowpark pipeline to transform mainframe data after initial staging.

B. Snowflake Tasks scheduled to periodically load data from external stages containing mainframe extracts, combined with materialized views for near real-time transformations.

C. Using external tables with direct access to the source systems, combined with stored procedures to transform the data on-read.

D. A third-party ETL tool such as Informatica or Matillion to extract, transform, and load data into Snowflake using JDBC/ODBC connections, scheduling the jobs for regular execution.

E. Snowpipe with auto-ingest enabled for flat files, Kafka Connector for REST API data streamed into Snowflake, and a custom Python script running as a Snowflake external function to process mainframe data dumps staged in cloud storage.

Correct Answer: A,E

Options A and E provide scalable and efficient solutions. Snowpipe with auto-ingest handles flat files efficiently. Kafka Connector allows real-time ingestion of REST API data. AWS Lambda and Snowflake external functions offer serverless computing for transforming mainframe data dumps into Snowflake. Option B relies on scheduled tasks and materialized views, which are not ideal for high-volume, near real- time ingestion. Option C using external tables for direct access to the source system might have performance challenges for real-time analysis. Option D relies on third-party ETL tools, adding to cost and complexity, and doesn't leverage Snowflake's native capabilities effectively.

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