MLS-C01 · Question #43
A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data. Which solution requires the…
The correct answer is B. Use AWS Glue to catalogue the data and Amazon Athena to run queries. Queries Against an Amazon S3 Data Lake Data lakes are an increasingly popular way to store and analyze both structured and unstructured data. If you want to build your own custom Amazon S3 data lake, AWS Glue can make all your data immediately available for analytics without…
Question
A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data. Which solution requires the LEAST effort to be able to query this data?
Options
- AUse AWS Data Pipeline to transform the data and Amazon RDS to run queries.
- BUse AWS Glue to catalogue the data and Amazon Athena to run queries.
- CUse AWS Batch to run ETL on the data and Amazon Aurora to run the queries.
- DUse AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.
How the community answered
(36 responses)- A8% (3)
- B69% (25)
- C6% (2)
- D17% (6)
Explanation
Queries Against an Amazon S3 Data Lake Data lakes are an increasingly popular way to store and analyze both structured and unstructured data. If you want to build your own custom Amazon S3 data lake, AWS Glue can make all your data immediately available for analytics without moving the data. https://aws.amazon.com/glue/
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