MLA-C01 · Question #159
A company has significantly increased the amount of data that is stored as .csv files in an Amazon S3 bucket. Data transformation scripts and queries are now taking much longer than they used to take.
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Question
A company has significantly increased the amount of data that is stored as .csv files in an Amazon S3 bucket. Data transformation scripts and queries are now taking much longer than they used to take. An ML engineer must implement a solution to optimize the data for query performance. Which solution will meet this requirement with the LEAST operational overhead?
Options
- AConfigure an AWS Lambda function to split the .csv files into smaller objects in the S3 bucket.
- BConfigure an AWS Glue job to drop columns that have string type values and to save the results
- CConfigure an AWS Glue extract, transform, and load (ETL) job to convert the .csv files to Apache
- DConfigure an Amazon EMR cluster to process the data that is in the S3 bucket.
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