This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
The Docker image is tagged incorrectly and cannot be pulled
Why wrong: If the image could not be pulled, the failure reason would be different (e.g., 'CannotPullContainer').
B
The training job is in a different region than the ECR repository
Why wrong: Region mismatch would cause a different error, not an argument error.
C
The input mode is File mode, but the container expects Pipe mode
Why wrong: Input mode does not affect argument parsing.
D
The custom Docker image does not use the SageMaker training toolkit and thus does not accept SageMaker hyperparameters
Custom containers that are not toolkit-based ignore SageMaker hyperparameters, causing unrecognized argument errors if the entry point tries to parse them.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
Under the hood, the SageMaker Training Toolkit wraps the user's training script and injects a default entry point (e.g., `python -m sagemaker_training.toolkit`) that intercepts SageMaker-specific arguments and maps them to environment variables (e.g., SM_PROGRAM, SM_CHANNELS). If the Docker image uses a custom entry point (e.g., `CMD python train.py`), it will receive the raw --sagemaker_program argument as an unknown flag. A real-world scenario is when a data scientist builds a container from a generic deep learning framework image (like TensorFlow or PyTorch) without adding the `sagemaker-training` package, causing the job to fail with this exact error.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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