AWS Certified Data Engineer Associate DEA-C01 Practice Question
A startup collects point-of-sale transactions from several third-party systems. Records are streamed to Amazon S3 with Amazon Kinesis Data Firehose and must be immediately accessible to partners that use many different analytics tools, some of which run outside AWS. The company wants a storage format that keeps ingestion latency and transformation effort as low as possible while remaining human-readable and easy to exchange. Which file format should the data engineer choose?
Convert the data to Apache Parquet before writing to Amazon S3.
Deliver the data in Apache ORC format to Amazon S3.
Store the records in CSV files on Amazon S3.
Serialize the records as Apache Avro files on Amazon S3.
Comma-separated values (CSV) files are plain text, human-readable, and universally supported by database, spreadsheet, and analytics tools both inside and outside AWS. Firehose can write the incoming records directly to S3 as CSV without any preprocessing, so ingestion latency stays minimal. Columnar formats such as Parquet or ORC and binary row-based formats like Avro deliver better query performance in services like Athena but require an additional transformation step and are not human-readable, increasing complexity and latency for downstream partners that need immediate access.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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