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Question 160 - MLS-C01 discussion

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A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.

Which storage option provides the most processing flexibility and will allow access control with IAM?

A.
Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.
Answers
A.
Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.
B.
Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.
Answers
B.
Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.
C.
Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.
Answers
C.
Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.
D.
Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.
Answers
D.
Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.
Suggested answer: B

Explanation:

The best storage option for the trucking company is to use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies. A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Amazon S3 is the ideal choice for building a data lake because it offers high durability, scalability, availability, and security. You can store any type of data in Amazon S3, such as images, videos, audio, text, etc. You can also use AWS services such as Amazon Rekognition, Amazon SageMaker, and Amazon EMR to analyze and process the data in the data lake. To ensure the data is only accessible to specific IAM users, you can use bucket policies to grant or deny access to the S3 buckets based on the IAM user's identity or role. Bucket policies are JSON documents that specify the permissions for the bucket and the objects in it. You can use conditions to restrict access based on various factors, such as IP address, time, source, etc. By using bucket policies, you can control who can access the data in the data lake and what actions they can perform on it.

References:

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Build a Data Lake Foundation with Amazon S3

AWS Machine Learning Training - Using Bucket Policies and User Policies

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asked 16/09/2024
Niels de Lange
38 questions
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