A data scientist receives a new dataset in .csv format and stores the dataset in Amazon S3. The data scientist will use the dataset to train a machine learning (ML) model. The data scientist first…

Question

Options

• ACreate an AWS Glue job to transform the data from .csv format to Apache Parquet format. Use
• BLeave the dataset in .csv format. Use an AWS Glue crawler and Amazon Athena with appropriate
• CCreate an AWS Glue job to transform the data from .csv format to Apache Parquet format. Import
• DLeave the dataset in .csv format. Import the data into Amazon SageMaker Data Wrangler. Use

• A
  12% (4)
• B
  3% (1)
• C
  6% (2)
• D
  79% (26)

Explanation

Amazon SageMaker Data Wrangler (D) is the correct answer because it natively supports importing CSV files directly from S3 without any format conversion. Its built-in 'Data Quality and Insights Report' automatically detects missing values, invalid or anomalous values, and outliers with minimal configuration - essentially a few clicks in a visual interface. This represents the least operational effort. Options A and C both require an AWS Glue ETL job to first transform the CSV into Parquet format before any analysis can begin - that is extra, unnecessary work. Option B uses an AWS Glue crawler plus Athena SQL queries, which requires writing queries and understanding the schema manually. Data Wrangler handles all of this automatically with its profile and data quality reporting features.

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