Exam AWS Certified Data Engineer - Associate DEA-C01 topic 1 question 59 discussion

Exam question from Amazon's AWS Certified Data Engineer - Associate DEA-C01

Question #: 59
Topic #: 1

[All AWS Certified Data Engineer - Associate DEA-C01 Questions]

A company needs to build a data lake in AWS. The company must provide row-level data access and column-level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon S3 for data lake storage. Use S3 access policies to restrict data access by rows and columns. Provide data access through Amazon S3.
B. Use Amazon S3 for data lake storage. Use Apache Ranger through Amazon EMR to restrict data access by rows and columns. Provide data access by using Apache Pig.
C. Use Amazon Redshift for data lake storage. Use Redshift security policies to restrict data access by rows and columns. Provide data access by using Apache Spark and Amazon Athena federated queries.
D. Use Amazon S3 for data lake storage. Use AWS Lake Formation to restrict data access by rows and columns. Provide data access through AWS Lake Formation.

Show Suggested Answer

Suggested Answer: D 🗳️

by rralucard_ at Feb. 2, 2024, 12:18 p.m.

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Comments

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Shanmahi

11 months, 1 week ago

Selected Answer: D

Using Amazon S3 for storage and AWS Lake Formation for fine-grained access control like row-level or column-level access.

upvoted 1 times

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cas_tori

11 months, 3 weeks ago

Selected Answer: D

this id D

upvoted 3 times

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Felix_G

1 year, 5 months ago

Option D is the best solution to meet the requirements with the least operational overhead. Using Amazon S3 for storage and AWS Lake Formation for access control and data access delivers the following advantages: S3 provides a highly durable, available, and scalable data lake storage layer Lake Formation enables fine-grained access control down to column and row-level Integrates natively with Athena, Redshift Spectrum, and EMR for simplified data access Fully managed service minimizes admin overhead vs self-managing Ranger or piecemeal solutions

upvoted 4 times

Felix_G

1 year, 5 months ago

Option A would require custom access control code development and greater ops effort Option B still requires managing Ranger integrated with EMR Option C does not natively support column-level security policies

upvoted 1 times

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rralucard_

1 year, 6 months ago

Selected Answer: D

https://docs.aws.amazon.com/lake-formation/latest/dg/cbac-tutorial.html Option D, using Amazon S3 for data lake storage and AWS Lake Formation for access control, is the most suitable solution. It meets the requirements for row-level and column-level access control and integrates well with Amazon Athena, Amazon Redshift Spectrum, and Apache Hive on EMR, all with lower operational overhead compared to the other options.

upvoted 4 times

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