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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 All Questions

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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 42 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01
Question #: 42
Topic #: 1
[All AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions]

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

  • A. Configure IAM policies on an AWS Glue Data Catalog to restrict access to Athena based on the ML engineers' campaigns.
  • B. Store users and campaign information in an Amazon DynamoDB table. Configure DynamoDB Streams to invoke an AWS Lambda function to update S3 bucket policies.
  • C. Use Lake Formation to authorize AWS Glue to access the S3 bucket. Configure Lake Formation tags to map ML engineers to their campaigns.
  • D. Configure S3 bucket policies to restrict access to the S3 bucket based on the ML engineers' campaigns.
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Suggested Answer: C 🗳️
by GiorgioGss at Nov. 27, 2024, 10:36 p.m.
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Saransundar
1 month ago
Selected Answer: C
AWS Lake Formation → Tag resources with campaigns → Map ML engineers to campaigns → Fine-grained access control → Operational efficiency
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GiorgioGss
1 month, 1 week ago
Selected Answer: C
"engineers must have access to only the resources that are specific to their assigned advertisement campaigns" This is a clear use-case for lakeformation
upvoted 1 times
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