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Question 68 - MLS-C01 discussion

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A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS

Which approach should the Specialist use for training a model using that data?

A.
Write a direct connection to the SQL database within the notebook and pull data in
Answers
A.
Write a direct connection to the SQL database within the notebook and pull data in
B.
Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
Answers
B.
Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
C.
Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in
Answers
C.
Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in
D.
Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
Answers
D.
Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
Suggested answer: B

Explanation:

Pushing the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and providing the S3 location within the notebook is the best approach for training a model using the data stored in Amazon RDS. This is because Amazon SageMaker can directly access data from Amazon S3 and train models on it. AWS Data Pipeline is a service that can automate the movement and transformation of data between different AWS services. It can also use Amazon RDS as a data source and Amazon S3 as a data destination. This way, the data can be transferred efficiently and securely without writing any code within the notebook.References:

Amazon SageMaker

AWS Data Pipeline

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asked 16/09/2024
Rajiesh George
39 questions
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