SAP-C02 · Question #385
A company is running an application in the AWS Cloud. The application collects and stores a large amount of unstructured data in an Amazon S3 bucket. The S3 bucket contains several terabytes of data…
The correct answer is C. Use an AWS Glue Data Catalog and Amazon Athena to query the data. Create an S3 Lifecycle. Generally, unstructured data should be converted structured data before querying them. AWS Glue can do that. https://docs.aws.amazon.com/glue/latest/dg/schema-relationalize.html https://docs.aws.amazon.com/athena/latest/ug/glue-athena.html
Question
A company is running an application in the AWS Cloud. The application collects and stores a large amount of unstructured data in an Amazon S3 bucket. The S3 bucket contains several terabytes of data and uses the S3 Standard storage class. The data increases in size by several gigabytes every day. The company needs to query and analyze the data. The company does not access data that is more than 1 year old. However, the company must retain all the data indefinitely for compliance reasons. Which solution will meet these requirements MOST cost-effectively?
Options
- AUse S3 Select to query the data. Create an S3 Lifecycle policy to transition data that is more than
- BUse Amazon Redshift Spectrum to query the data. Create an S3 Lifecycle policy to transition data
- CUse an AWS Glue Data Catalog and Amazon Athena to query the data. Create an S3 Lifecycle
- DUse Amazon Redshift Spectrum to query the data. Create an S3 Lifecycle policy to transition data
How the community answered
(24 responses)- A4% (1)
- B4% (1)
- C79% (19)
- D13% (3)
Explanation
Generally, unstructured data should be converted structured data before querying them. AWS Glue can do that. https://docs.aws.amazon.com/glue/latest/dg/schema-relationalize.html https://docs.aws.amazon.com/athena/latest/ug/glue-athena.html
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