AWS Certified Data Engineer Associate DEA-C01 Practice Question
A company ingests hourly CSV files from thousands of IoT sensors into an Amazon S3 location registered with AWS Lake Formation. Analysts run Amazon Athena queries covering the last 30 days and occasionally restrict results to a single sensor ID. As the data engineer, how should you register the dataset in the AWS Glue Data Catalog to achieve the lowest query cost and latency?
Register an unpartitioned Apache Iceberg table secured by Lake Formation and convert the files to Snappy-compressed Parquet.
Register an Apache Iceberg table partitioned by sensor_id and then by date, storing data in Snappy-compressed Parquet.
Register an Apache Iceberg table partitioned by date (yyyy/mm/dd) and store the data in Snappy-compressed Parquet.
Create a Hive-compatible external table partitioned only by sensor_id and keep the files in their original CSV format.
Partitioning on a low-cardinality time column lets Athena prune partitions quickly. Using a daily date key (yyyy/mm/dd) keeps the look-back window to only 30 partitions, while partitioning by sensor ID would create thousands of tiny partitions and slow planning. Converting the files to columnar Parquet with Snappy compression further reduces the number of bytes scanned. Registering the data as an Apache Iceberg table provides Lake Formation security and ACID transactions without changing the partition design. The other options either omit partitioning, use a high-cardinality partition key, or retain inefficient CSV files, all of which increase cost and latency.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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