MLS-C01 · Question #147
A Machine Learning Specialist stores IoT soil sensor data in an Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the…
The correct answer is D. Crawl the data using AWS Glue crawlers. The specialist needs to combine and prepare data from DynamoDB and S3 for SageMaker training with minimal administrative effort. AWS Glue crawlers are the ideal serverless solution for discovering schema and cataloging disparate datasets.
Question
A Machine Learning Specialist stores IoT soil sensor data in an Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The Specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker. Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?
Options
- ALaunch an Amazon EMR cluster.
- BCrawl the data using AWS Glue crawlers.
- CEnable Amazon DynamoDB Streams on the sensor table.
- DCrawl the data using AWS Glue crawlers.
How the community answered
(24 responses)- A13% (3)
- B4% (1)
- C4% (1)
- D79% (19)
Why each option
The specialist needs to combine and prepare data from DynamoDB and S3 for SageMaker training with minimal administrative effort. AWS Glue crawlers are the ideal serverless solution for discovering schema and cataloging disparate datasets.
Launching an Amazon EMR cluster requires significant administrative overhead for cluster provisioning, configuration, and management, which is not aligned with the 'least amount of administrative overhead' requirement.
This choice is a duplicate of option D and therefore represents the same correct approach.
Enabling Amazon DynamoDB Streams primarily captures change-data-capture (CDC) events for real-time processing, not for batch schema inference and cataloging of an existing large dataset from both DynamoDB and S3 for SageMaker training.
AWS Glue crawlers automatically infer schemas from data stored in various sources like Amazon S3 and Amazon DynamoDB, then populate the AWS Glue Data Catalog. This serverless process reduces administrative overhead significantly, allowing SageMaker to easily access and process the data without manual schema definition or cluster management.
Concept tested: Data cataloging and preparation with AWS Glue
Source: https://docs.aws.amazon.com/glue/latest/dg/components-overview.html#data-catalog-and-crawlers
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