A company is using Amazon SageMaker to train a model on a dataset that is updated daily. The data is stored in an S3 bucket. The training pipeline uses AWS Step Functions to orchestrate data preprocessing and model training. The preprocessing step uses a SageMaker Processing job that reads data from S3, cleans it, and writes the output back to S3. The team notices that the training step often fails due to insufficient disk space on the processing instance. Which change should the team make to resolve this issue without increasing cost?

Question

Accepted Answer

Configure the processing job to use local instance store (SSD) for scratch space.. The issue is insufficient disk space on the processing instance. Option D resolves this by configuring the processing job to use the local instance store (SSD) for scratch space, which provides high-throughput temporary storage without incurring additional cost, as the instance store is included with the instance. This allows the preprocessing step to handle larger intermediate data without requiring a larger or more expensive instance.

Answer

Enable automatic scaling for the processing job.

Answer

Use AWS Batch instead of SageMaker Processing.

Answer

Use a larger instance type with more memory.

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

MLS-C01 Data Engineering Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

Question Discussion