AIF-C01 Fundamentals of AI and ML Practice Question
A company wants to use Amazon SageMaker to train a model using a custom Docker container that has specific dependencies. The training code is stored in an S3 bucket. Which steps must be taken to run the training job?
⚠ Common exam trap
AWS often tests the misconception that any S3-uploaded artifact (including Docker images) can be directly referenced in a training job, but SageMaker strictly requires container images to be stored in ECR, not S3.
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 custom container to Amazon ECR and create a training job with the container URI
Amazon SageMaker requires custom Docker containers to be stored in Amazon Elastic Container Registry (ECR) to run training jobs. The container URI from ECR is specified in the `AlgorithmSpecification` parameter of the `CreateTrainingJob` API call, allowing SageMaker to pull and execute the container with the training code from S3. Option B correctly describes this mandatory workflow.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Install dependencies via SageMaker's lifecycle configuration instead of a custom container
Why it's wrong here
Lifecycle configurations run shell scripts when an instance starts, which cannot replace the image's dependency stack or guarantee the exact library versions the training code needs. They suit installing notebook extensions or mounting file systems; a custom Docker container is required when dependencies must be baked into the image itself.
- ✓
Push the custom container to Amazon ECR and create a training job with the container URI
Why this is correct
Pushing the image to Amazon ECR gives SageMaker a registry URI it can pull from, satisfying the custom-dependency requirement. The training job then references that image URI alongside the S3 code location, so SageMaker runs your container rather than a built-in algorithm image.
- ✗
Use SageMaker's built-in framework container and override the entry point
Why it's wrong here
Overriding a built-in framework container's entry point still inherits that image's preinstalled libraries, so conflicting or missing dependencies cannot be resolved. This approach suits standard frameworks where only the training script path differs; a custom container is needed when the image's dependency set itself must be controlled.
- ✗
Upload the container to S3 and reference it in the training job
Why it's wrong here
SageMaker training jobs pull container images from Amazon ECR, not from S3, so an image stored in S3 cannot be referenced by the estimator. Uploading to S3 suits training data and model artefacts; the container image must be pushed to an ECR repository in the same Region.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.