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Amazon Exam DAS-C01 Topic 7 Question 87 Discussion

Actual exam question for Amazon's DAS-C01 exam
Question #: 87
Topic #: 7
[All DAS-C01 Questions]

A company wants to use a data lake that is hosted on Amazon S3 to provide analytics services for historical dat

a. The data lake consists of 800 tables but is expected to grow to thousands of tables. More than 50 departments use the tables, and each department has hundreds of users. Different departments need access to specific tables and columns.

Which solution will meet these requirements with the LEAST operational overhead?

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Suggested Answer: C

by Britt at Mar 09, 2024, 05:57 AM

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Nobuko
11 months ago
I think option D) may result in too much overhead. Creating an EMR cluster for each department sounds like it could get complex.
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Kanisha
11 months ago
I personally prefer option C). Using tag-based access control with AWS Lake Formation and attaching LF-tags to tables and columns seems like a more organized approach.
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Mari
12 months ago
Option B) also seems reasonable. Creating a Redshift cluster for each department and ingesting relevant data could work well too.
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Carlene
12 months ago
I agree with you, Chu. Using AWS Lake Formation for access control and granting specific access to tables and columns makes sense.
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Chu
12 months ago
I think option A) sounds like a good solution. Creating an 1AM role for each department seems like a good way to manage access.
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Chuck
1 years ago
I see your point, Shelia. Option A does seem to be a better long-term solution for managing access to a growing number of tables.
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Shelia
1 years ago
That's true, but I think using Lake Formation for access control in option A is more scalable as the data lake grows.
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Josephine
1 years ago
But what about option B? It also provides access control at the department level by using Redshift clusters.
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Chuck
1 years ago
I agree with Shelia. Option A seems to be the most efficient in terms of managing access to tables and columns.
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Shelia
1 years ago
I think option A sounds like a good choice. It allows for specific access control for each department.
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Laurena
1 years ago
Option B sounds like a lot of work, though. Setting up and managing multiple Redshift clusters? No thanks. I think the simplicity of option C, with Lake Formation, is the way to go. Plus, Athena is a great tool for ad-hoc analytics.
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Cherilyn
1 years ago
I'm not sure about that. Option B, using Amazon Redshift, could also work well. That way, we can isolate the data and access for each department, and use Redshift's robust security features. It might be a bit more hands-on, but it could give us more control.
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Torie
1 years ago
I agree, option C seems like the most scalable and least operationally intensive solution. Creating individual IAM roles for each department would be a nightmare to manage as the data lake grows. The tag-based access control in Lake Formation should give us the flexibility we need.
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Desmond
1 years ago
I think we've found our solution with option C. It's scalable and operationally efficient.
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Pilar
1 years ago
Agreed, managing IAM roles for each department as the data lake grows would be a nightmare.
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Silvana
1 years ago
It definitely seems like the most flexible option for granting access to specific resources.
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Fernanda
1 years ago
I like the idea of creating LF-tags for tables and columns to grant access to departments.
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Desiree
1 years ago
Using tag-based access control in Lake Formation seems like a much more efficient solution.
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Yoko
1 years ago
I agree, creating individual IAM roles for each department would be too cumbersome.
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Fannie
1 years ago
I think option C is the best choice for scalability and ease of management.
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Jerry
1 years ago
Hmm, this is a tricky question. We need to consider the scale of the data lake and the need for granular access control across multiple departments. I'm leaning towards option C - using AWS Lake Formation tag-based access control. That way, we can easily manage access to specific tables and columns without creating a ton of IAM roles.
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