Answer is B as the data for a SageMaker notebook needs to be from S3 and option B is the only option that says it. The only thing with option B is that it is talking of moving data from MS SQL Server not RDS

For Amazon S3, you can import data from an Amazon S3 bucket as long as you have permissions to access the bucket. For Amazon Athena, you can access databases in your AWS Glue Data Catalog as long as you have permissions through your Amazon Athena workgroup. For Amazon RDS, if you have the AmazonSageMakerCanvasFullAccess policy attached to your user’s role, then you’ll be able to import data from your Amazon RDS databases into Canvas. https://docs.aws.amazon.com/sagemaker/latest/dg/canvas-connecting-external.html

A. NO - SageMaker can only read from S3 B. YES - AWS Data Pipeline can moved from SQL Server to S3 C. NO - SageMaker can only read from S3 and not DynamoDB D. NO - SageMaker can only read from S3 and not ElastiCache

This approach is the most scalable and reliable way to train a model using data stored in Amazon RDS. Amazon S3 is a highly scalable and durable object storage service, and Amazon Data Pipeline is a managed service that makes it easy to move data between different AWS services. By pushing the data to Amazon S3, the Specialist can ensure that the data is available for training the model even if the Amazon RDS instance is unavailable.

I think it should be b

It's B, even if Microsoft SQL Server is a strange name for RDS, it's a possible database to use there and the data for sagemaker needs to be in S3!

In Option B approach, the Specialist can use AWS Data Pipeline to automate the movement of data from Amazon RDS to Amazon S3. This allows for the creation of a reliable and scalable data pipeline that can handle large amounts of data and ensure the data is available for training. In the Amazon SageMaker notebook, the Specialist can then access the data stored in Amazon S3 and use it for training the model. Using Amazon S3 as the source of training data is a common and scalable approach, and it also provides durability and high availability of the data.

B is the correct answer. Official AWS Documentation: "Amazon ML allows you to create a datasource object from data stored in a MySQL database in Amazon Relational Database Service (Amazon RDS). When you perform this action, Amazon ML creates an AWS Data Pipeline object that executes the SQL query that you specify, and places the output into an S3 bucket of your choice. Amazon ML uses that data to create the datasource."

While B is a valid answer, It is also possible to make a SQL connection in a notebook and create a data object so A could be a valid answer too https://stackoverflow.com/questions/36021385/connecting-from-python-to-sql-server https://www.mssqltips.com/sqlservertip/6120/data-exploration-with-python-and-sql-server-using-jupyter-notebooks/

B is correct. MS SQL Server is also under RDS.

B is right

B it is

I'll go with B