Amazon Exam DAS-C01 Topic 2 Question 83 Discussion

Actual exam question for Amazon's DAS-C01 exam

Question #: 83
Topic #: 2

An education provider's learning management system (LMS) is hosted in a 100 TB data lake that is built on Amazon S3. The provider's LMS supports hundreds of schools. The provider wants to build an advanced analytics reporting platform using Amazon Redshift to handle complex queries with optimal performance. System users will query the most recent 4 months of data 95% of the time while 5% of the queries will leverage data from the previous 12 months.

Which solution meets these requirements in the MOST cost-effective way?

AStore the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query data in the data lake. Use S3 lifecycle management rules to store data from the previous 12 months in Amazon S3 Glacier storage.

BLeverage DS2 nodes for the Amazon Redshift cluster. Migrate all data from Amazon S3 to Amazon Redshift. Decommission the data lake.

CStore the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query data in the data lake. Ensure the S3 Standard storage class is in use with objects in the data lake.

DStore the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift federated queries to join cluster data with the data lake to reduce costs. Ensure the S3 Standard storage class is in use with objects in the data lake.

Show Suggested Answer

Suggested Answer: A

by Lai at Dec 14, 2023, 06:23 AM

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Pearlene

1 years ago

Haha, I hear you. Spark can be a bit intimidating, but it's worth it in the long run. Although, I have to say, option D also seems like a decent choice. Stored procedures in Redshift can be pretty efficient, and we wouldn't have to worry about the complexity of Spark. Decisions, decisions...

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1 years ago

Hmm, this is a tricky one. We need to find a way to speed up the response time without too much implementation effort. I'm leaning towards option C - creating a materialized view in Amazon Redshift. That way, the data is already pre-joined and ready for QuickSight to use, which should improve the performance.

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