The answer is that the training script fails to save the model to the required /opt/ml/model directory. This is the correct choice because Amazon SageMaker is designed to automatically copy all contents from the /opt/ml/model directory to the specified S3 output path once the training job completes. If your script saves the model artifacts to a different location, such as /tmp or a custom path, SageMaker has no mechanism to locate or upload those files, resulting in the artifacts not being saved to S3 despite a successful training run. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker’s container architecture and the expected directory structure for training jobs. A common trap is assuming SageMaker automatically captures any model file generated during training, but the service only monitors the designated /opt/ml/model folder. Memory tip: think “/opt/ml/model or the model is gone” — SageMaker only looks in that one spot.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
A company uses Amazon SageMaker to train a model. The training job runs successfully but the model artifacts are not saved to the specified S3 output path. What is a likely cause?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The training script does not save the model to /opt/ml/model.
Option A is correct because Amazon SageMaker expects the training script to save the model artifacts to the `/opt/ml/model` directory. After the training job completes, SageMaker automatically copies the contents of this directory to the specified S3 output path. If the script saves the model elsewhere (e.g., `/tmp` or a custom path), no artifacts will be uploaded, resulting in an empty or missing S3 output.
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 training script does not save the model to /opt/ml/model.
Why this is correct
SageMaker uploads contents of /opt/ml/model to S3; saving elsewhere means artifacts are lost.
Related concept
Read the scenario before looking for a memorised answer.
✗
The model size exceeds the S3 bucket limit.
Why it's wrong here
S3 has no size limit; there is a per-object limit of 5TB.
✗
The training job used spot instances.
Why it's wrong here
Spot instances may be interrupted, but if job completes, artifacts are saved.
✗
The S3 bucket is in a different AWS Region.
Why it's wrong here
SageMaker can access buckets in other regions with proper permissions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume any successful training job automatically saves artifacts, but SageMaker only uploads what is explicitly placed in `/opt/ml/model`, and the exam tests this specific SageMaker convention.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker runs the training container with a Docker volume mount that maps `/opt/ml/model` to a local directory. After the script exits, SageMaker uses the AWS CLI to sync this directory to the S3 output path. A common subtlety is that if the script writes to a subdirectory (e.g., `/opt/ml/model/subdir/model.pth`), SageMaker will upload the entire `/opt/ml/model` tree, so the S3 path will contain the subdirectory structure. In real-world scenarios, data scientists often forget to include a `model.save()` call or save to a hardcoded path like `/outputs/model.pth`, leading to missing artifacts despite a successful training run.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 training script does not save the model to /opt/ml/model. — Option A is correct because Amazon SageMaker expects the training script to save the model artifacts to the `/opt/ml/model` directory. After the training job completes, SageMaker automatically copies the contents of this directory to the specified S3 output path. If the script saves the model elsewhere (e.g., `/tmp` or a custom path), no artifacts will be uploaded, resulting in an empty or missing S3 output.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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