MLA-C01 · Question #144
A company is developing a new ML model that uses the XGBoost algorithm. The company will train the model on data that is stored in an Amazon S3 bucket. The data is in a nested JSON format. An ML…
The correct answer is A. Create an AWS Glue PySpark job that uses the Relationalize transform to convert the files. The AWS Glue PySpark Relationalize transform is purpose-built to convert nested JSON into tabular format with minimal operational overhead. It automates the flattening process without requiring custom code or complex infrastructure, making it the most efficient solution for…
Question
A company is developing a new ML model that uses the XGBoost algorithm. The company will train the model on data that is stored in an Amazon S3 bucket. The data is in a nested JSON format. An ML engineer needs to convert the JSON files into a tabular format. Which solution will meet this requirement with the LEAST operational overhead?
Options
- ACreate an AWS Glue PySpark job that uses the Relationalize transform to convert the files.
- BWrite custom Scala code to convert the files. Use Amazon EMR Serverless to run the Scala code.
- CCreate an AWS Lambda function that uses a Python runtime and invokes the reduce() function to
- DCreate an Amazon Athena database that is based on the JSON files. Use the Athena flatten
How the community answered
(36 responses)- A81% (29)
- B3% (1)
- C6% (2)
- D11% (4)
Explanation
The AWS Glue PySpark Relationalize transform is purpose-built to convert nested JSON into tabular format with minimal operational overhead. It automates the flattening process without requiring custom code or complex infrastructure, making it the most efficient solution for preparing the data for XGBoost training.
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