MLA-C01 ML Model Development Practice Question
A machine learning engineer is using a SageMaker training job with a custom training script. They need to save the trained model artifacts to Amazon S3 so that the model can be deployed later. Which parameter in the SageMaker estimator should they configure to specify the S3 location for model artifacts?
⚠ Common exam trap
Test-takers frequently confuse model_dir, which controls the local directory for saving the model inside the container, with output_path, which determines the final S3 location for artifacts.
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
✓
output_path
The output_path parameter in the SageMaker estimator defines the S3 URI where training job artifacts, including the final model, are stored. It allows control over the bucket and prefix, which is essential for governance and deployment. Other parameters like model_dir, code_location, and dependencies serve different purposes in the training lifecycle.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
code_location
Why it's wrong here
code_location specifies the S3 location where SageMaker uploads the training code (the sourcedir) for the training job. It is not used for storing model artifacts. While it is an S3 path, it serves a different purpose: hosting the training script and dependencies. Configuring it will not direct the output model artifacts to the desired location.
- ✗
model_dir
Why it's wrong here
model_dir is a parameter used in some frameworks to specify the local directory where the training script should save the model. It does not control the S3 location for artifacts; SageMaker automatically uploads the contents of model_dir to S3. Setting model_dir alone does not determine the final S3 path, so it is not the correct parameter for this requirement.
- ✓
output_path
Why this is correct
The output_path parameter in a SageMaker estimator specifies the S3 location where the training job stores model artifacts, such as model.tar.gz. It is the correct way to direct the trained model to a desired S3 bucket and prefix. Without setting it, artifacts go to a default SageMaker-managed bucket, which may not meet organizational requirements.
- ✗
dependencies
Why it's wrong here
dependencies is a list of local directories or files that SageMaker uploads to S3 and makes available in the training container. It is used for additional code or data, not for specifying the output artifact location. Setting dependencies does not influence where the trained model is saved, so it is irrelevant to the requirement.
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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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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