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Amazon MLA-C01 Exam - Topic 4 Question 9 Discussion

Actual exam question for Amazon's MLA-C01 exam
Question #: 9
Topic #: 4
[All MLA-C01 Questions]

An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured dat

a. The company's ML engineers are assigned to specific advertisement campaigns.

The ML engineers must interact with the data through Amazon Athena and by browsing the data directly in an Amazon S3 bucket. The ML engineers must have access to only the resources that are specific to their assigned advertisement campaigns.

Which solution will meet these requirements in the MOST operationally efficient way?

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Suggested Answer: C

by Fletcher at Jul 31, 2025, 05:54 AM

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Dottie
3 months ago
B is overcomplicating things, just use Lake Formation!
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Marshall
3 months ago
I disagree, I think D could work just as well.
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Nathan
3 months ago
IAM policies alone won't cut it, C is definitely the way to go!
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Jess
4 months ago
Wait, can Lake Formation really handle that? Sounds too good to be true.
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Aimee
4 months ago
Option C seems like the best fit for managing access efficiently.
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Victor
4 months ago
I feel like option B is overcomplicating things with DynamoDB and Lambda. I think C is definitely the most operationally efficient solution here.
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Tiera
4 months ago
I practiced a similar question where we had to restrict access based on user roles. I think option D could be a bit cumbersome compared to using Lake Formation.
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Chantell
4 months ago
I'm not entirely sure, but I think IAM policies could work too. Option A sounds familiar, but it might not be as streamlined as using Lake Formation.
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Jodi
5 months ago
I remember we discussed using Lake Formation tags to manage access efficiently. It seems like option C might be the best fit for this scenario.
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Viva
5 months ago
I'm leaning towards the Lake Formation solution. Using tags to map engineers to their campaigns seems like a clean and scalable way to manage access. Plus, it integrates directly with the data lake.
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Anika
5 months ago
Okay, I think the key here is restricting access based on the ML engineers' assigned campaigns. The DynamoDB Streams and Lambda function approach seems interesting, but I'm not sure if that's the most efficient.
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Teddy
5 months ago
Hmm, I'm a bit confused about the difference between using IAM policies on the Glue Data Catalog versus using Lake Formation tags. I'll need to review those options closely.
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Jesusita
5 months ago
This looks like a tricky one. I'll need to carefully read through the requirements and think about the most operationally efficient solution.
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Tish
6 months ago
I see your point, Shantell. Option C does seem like a good solution to ensure the ML engineers have access only to resources specific to their campaigns.
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Shantell
7 months ago
I disagree, I believe option C is more efficient as it authorizes AWS Glue to access the S3 bucket and maps ML engineers to their campaigns using Lake Formation tags.
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Carin
7 months ago
I'm leaning towards Option B. Using DynamoDB Streams and Lambda to update S3 policies could be a more flexible solution.
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Jarvis
7 months ago
I think option A is the best choice because it allows us to restrict access to Athena based on the ML engineers' campaigns.
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Mariann
7 months ago
Option C seems the most straightforward. Lake Formation can handle the access control and tag-based permissions for us.
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Herminia
6 months ago
It definitely simplifies the process and ensures that the ML engineers only have access to the resources specific to their campaigns.
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Tequila
7 months ago
I agree, using Lake Formation to authorize AWS Glue and configuring tags for the ML engineers' campaigns sounds efficient.
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Kimbery
7 months ago
Option C seems the most straightforward. Lake Formation can handle the access control and tag-based permissions for us.
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