DEA-C01 · Question #163
A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day. The company stores all order data at a single path
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Question
A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day. The company stores all order data at a single path within the S3 bucket. The data has more than 100 columns. The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size. The company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded. A data engineer must resolve the performance issues for the queries. Which combination of steps will meet this requirement with LEAST developmental effort? (Choose two.)
Options
- AConfigure the third-party application to create the files in a columnar format.
- BDevelop an AWS Glue ETL job to convert the multiple daily CSV files to one file for each day.
- CPartition the order data in the S3 bucket based on order date.
- DConfigure the third-party application to create the files in JSON format.
- ELoad the JSON data into the Amazon Redshift table in a SUPER type column.
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