MLA-C02 practice question: Ingesting and storing ML data (2 of 2)
Data Preparation for ML and AI A free question from our original MLA-C02 practice exam. Try it, then open the answer to see why each option is right or wrong.
A dental chain stores about 9 TB of anonymized X-ray images and their labels in Amazon EFS. Analysts and SageMaker notebooks already mount this file system, and the images are organized in a folder tree the team wants to keep.
The chain's data science team plans to train an image classifier with a SageMaker training job in the same VPC. They want to avoid copying the images into another store before each run, and they need the job to read the files directly.
Which approach meets these requirements?
Choose one answer.
AA. Use AWS DataSync to copy the EFS file system into an Amazon S3 bucket every night, and point the training job's input channel at that bucket.
BB. Set the training channel to the EFS file system as a file system data source, and run the job in a subnet that can reach the file system.
CC. Copy the folder tree from a notebook instance to the training container with a shell script before each job starts, using the instance's local disk.
DD. Move the images into an Amazon DynamoDB table with one item per file, and read them from the table inside the training script.
B. Right: Correct. SageMaker AI can mount an Amazon EFS file system as a training data source and then start the training script, so the images are read in place. The training job must connect to a VPC that can reach the file system. Learn more: Setting up training jobs to access datasets (opens in a new tab)
C. Wrong: Copying the whole tree on every run adds time and depends on each notebook having a complete copy. The training job also would not benefit from the shared file system.
D. Wrong: DynamoDB items are capped in size, so large image files do not fit well, and reading 9 TB through item requests is slow and costly. The file system already holds the data in the right form.
Explanation
SageMaker AI training can use an Amazon EFS file system as a data source, so the job reads the images in place without a nightly copy. The job must run in a VPC subnet that can reach the file system. Copying the data to S3 or local disks adds delay and duplicates storage, and DynamoDB is a poor fit for large image files.
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