MLA-C01 ML Model Development Practice Question

A machine learning engineer is preparing a training job on SageMaker with a custom Docker container. Which TWO actions are required to use the container with SageMaker? (Choose TWO.)

⚠ Common exam trap

Watch out — candidates often confuse the storage location for container images (ECR) with other AWS storage services like S3, and assuming that optional monitoring or registry features are required for custom container usage.

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

✓

Push the container image to Amazon ECR

Option A is correct because SageMaker can only pull custom training container images from Amazon ECR, so the image must be built and pushed to an ECR repository that the SageMaker execution role can access. Option B is correct because the SageMaker Estimator must be configured with the image_uri parameter set to the ECR image URI (for example, <account>.dkr.ecr.<region>.amazonaws.com/<repo>:<tag>) so SageMaker knows which container to run for training. Option C is incorrect because container images cannot be stored or executed from Amazon S3; S3 is used for training data and model artifacts, not Docker images. Option D is incorrect because SageMaker Debugger is an optional monitoring and profiling feature, not a requirement for using a custom container. Option E is incorrect because the SageMaker Model Registry is used to catalog trained models for governance and deployment, and is not needed to run a custom training container.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Push the container image to Amazon ECR
Why this is correct
SageMaker pulls custom training images from Amazon ECR, so the image must be pushed there and its URI supplied to the estimator. This satisfies the requirement to host the container where SageMaker can access it.
✓
Use a SageMaker Estimator with image_uri parameter pointing to the ECR image
Why this is correct
SageMaker needs to know which image to run, and the Estimator's image_uri parameter supplies the Amazon ECR path to the custom container. This registers the container with the training job so SageMaker can pull and execute it.
✗
Upload the container image to Amazon S3
Why it's wrong here
SageMaker pulls training images from Amazon ECR, not from Amazon S3, which stores datasets, model artefacts and output rather than container images. It is tempting because S3 underpins nearly every SageMaker workflow, but the required action is pushing the image to ECR with the correct repository permissions.
✗
Enable SageMaker Debugger to monitor the custom container
Why it's wrong here
Debugger monitors tensors and metrics during training; it is an optional observability feature, not a prerequisite for launching a custom container. It is tempting because Debugger is commonly enabled alongside custom training jobs, but the mandatory actions are pushing the image to Amazon ECR and specifying the training algorithm name.
✗
Register the container in SageMaker Model Registry
Why it's wrong here
Model Registry stores trained model versions for deployment and governance; it plays no part in making a custom training image runnable. It is tempting because registry registration is mandatory later in the MLOps lifecycle, but the training job itself requires the image in Amazon ECR and the algorithm name specified.

Quick reference

AWS S3 Storage Class Comparison

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

Go deeper

MLA-C01 ML Model Development Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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