AWS Certified Machine Learning - Specialty MLS-C01 — Topic 1 Question 293

Question

A data scientist stores financial datasets in Amazon S3. The data scientist uses Amazon Athena to query the datasets by using SQL. The data scientist uses Amazon SageMaker to deploy a machine learning (ML) model. The data scientist wants to obtain inferences from the model at the SageMaker endpoint. However, when the data scientist attempts to invoke the SageMaker endpoint, the data scientist receives SQL statement failures. The data scientist’s IAM user is currently unable to invoke the SageMaker endpoint. Which combination of actions will give the data scientist’s IAM user the ability to invoke the SageMaker endpoint? (Choose three.)

Accepted Answer

Correct answer: B, C, E. The correct answer is B, C, E. Option B: Include a policy statement for the data scientist's IAM user that allows the IAM user to perform the sagemaker:InvokeEndpoint action. Option C: Include an inline policy for the data scientist’s IAM user that allows SageMaker to read S3 objects. Option E: Include the SQL statement "USING EXTERNAL FUNCTION ml_function_name'' in the Athena SQL query. Amazon S3 provides durable, scalable object storage that is fully managed. Amazon Athena runs serverless SQL queries directly on data in Amazon S3 with no infrastructure to manage. Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

AWS Certified Machine Learning - Specialty MLS-C01 — Question 293