MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning pipeline uses SageMaker Processing jobs for feature engineering. Which TWO are benefits of using SageMaker Processing over running a custom script on an EC2 instance?
⚠ Common exam trap
Test-takers frequently confuse SageMaker Processing's ability to use custom Docker images with support for any registry, but the service strictly requires images to be hosted in Amazon ECR.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Automatically manages the compute resources
SageMaker Processing automatically manages the underlying compute resources, including provisioning, scaling, and terminating instances. This eliminates the need for manual infrastructure management, which is required when running a custom script on an EC2 instance. Option B is correct because SageMaker Processing jobs natively integrate with SageMaker Experiments, allowing automatic tracking of parameters, metrics, and artifacts for reproducibility and comparison.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Automatically manages the compute resources
Why this is correct
SageMaker provisions and tears down resources.
- ✓
Integrates with SageMaker Experiments for tracking
Why this is correct
Processing jobs can be tracked as experiments.
- ✗
Provides a built-in VPC for network isolation
Why it's wrong here
EC2 can also be in a VPC.
- ✗
Allows use of custom Docker images from any registry
Why it's wrong here
EC2 also allows any Docker image.
- ✗
Supports multiple programming languages
Why it's wrong here
EC2 also supports multiple languages.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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