MLS-C01 Modeling Practice Question
Network Topology
A data scientist is troubleshooting a failed SageMaker training job that uses a custom Docker image. The failure reason shows 'unrecognized arguments: --sagemaker_program'. What is the most likely cause?
⚠ Common exam trap
Watch out — candidates often confuse container-level errors (like pull failures or region mismatches) with argument parsing errors, failing to recognize that the SageMaker Training Toolkit is required to handle SageMaker-specific CLI arguments.
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
✓
The custom Docker image does not use the SageMaker training toolkit and thus does not accept SageMaker hyperparameters
The error 'unrecognized arguments: --sagemaker_program' indicates that the custom Docker image does not include the SageMaker Training Toolkit. The SageMaker Training Toolkit is a Python library that provides a default entry point to parse and handle SageMaker-specific hyperparameters (like --sagemaker_program, --sagemaker_submit_directory, etc.). Without this toolkit, the container's entry point does not recognize these arguments, causing the training job to fail.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The Docker image is tagged incorrectly and cannot be pulled
Why it's wrong here
If the image could not be pulled, the failure reason would be different (e.g., 'CannotPullContainer').
- ✗
The training job is in a different region than the ECR repository
Why it's wrong here
Region mismatch would cause a different error, not an argument error.
- ✗
The input mode is File mode, but the container expects Pipe mode
Why it's wrong here
Input mode does not affect argument parsing.
- ✓
The custom Docker image does not use the SageMaker training toolkit and thus does not accept SageMaker hyperparameters
Why this is correct
Custom containers that are not toolkit-based ignore SageMaker hyperparameters, causing unrecognized argument errors if the entry point tries to parse them.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-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 MLS-C01 exam.