MLS-C01 · Question #358
A company maintains a 2 TB dataset that contains information about customer behaviors. The company stores the dataset in Amazon S3. The company stores a trained model container in Amazon Elastic Conta
The correct answer is B. Score the model by using AWS Batch managed Amazon EC2 Spot Instances. Create an Amazon. For scalable and cost-effective batch scoring of a 2 TB dataset, leveraging AWS Batch with EC2 Spot Instances is the optimal solution due to its managed nature and significant cost savings for interruptible workloads.
Question
A company maintains a 2 TB dataset that contains information about customer behaviors. The company stores the dataset in Amazon S3. The company stores a trained model container in Amazon Elastic Container Registry (Amazon ECR). A machine learning (ML) specialist needs to score a batch model for the dataset to predict customer behavior. The ML specialist must select a scalable approach to score the model. Which solution will meet these requirements MOST cost-effectively?
Options
- AScore the model by using AWS Batch managed Amazon EC2 Reserved Instances. Create an
- BScore the model by using AWS Batch managed Amazon EC2 Spot Instances. Create an Amazon
- CScore the model by using an Amazon SageMaker notebook on Amazon EC2 Reserved
- DScore the model by using Amazon SageMaker notebook on Amazon EC2 Spot Instances. Create
How the community answered
(48 responses)- A6% (3)
- B73% (35)
- C17% (8)
- D4% (2)
Why each option
For scalable and cost-effective batch scoring of a 2 TB dataset, leveraging AWS Batch with EC2 Spot Instances is the optimal solution due to its managed nature and significant cost savings for interruptible workloads.
While AWS Batch is suitable for batch processing, using EC2 Reserved Instances is less cost-effective for bursty or temporary batch jobs compared to Spot Instances, as RIs are designed for predictable, long-term workloads.
AWS Batch is a fully managed service suitable for scalable batch processing, and using EC2 Spot Instances offers significant cost savings (up to 90% compared to On-Demand) for fault-tolerant workloads like batch model scoring, making it the most cost-effective and scalable solution.
An Amazon SageMaker notebook is an interactive environment primarily for model development and experimentation, not for running scalable, production-grade batch scoring jobs on large datasets like 2 TB.
Using an Amazon SageMaker notebook for batch scoring is inappropriate for production workloads, and while Spot Instances reduce cost, the notebook environment itself is not designed for scalable batch processing of large datasets.
Concept tested: Cost-effective batch inference with AWS Batch
Source: https://aws.amazon.com/batch/
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