- A
Expose a REST API endpoint for inference
Why wrong: Training containers do not need to expose endpoints.
- B
Implement the train function in the container that saves model artifacts to /opt/ml/model
SageMaker expects the model to be saved in /opt/ml/model.
- C
Define a Docker Compose file to manage multi-container training
Why wrong: SageMaker does not use Docker Compose.
- D
Include a training script that reads hyperparameters from /opt/ml/input/config/hyperparameters.json
SageMaker passes hyperparameters in this file.
- E
Push the container image to Docker Hub
Why wrong: SageMaker requires the image to be in Amazon ECR.
Question 620 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
Which TWO configuration steps are necessary to deploy a custom Docker container for training in Amazon SageMaker? (Choose two.)
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
✓
Implement the train function in the container that saves model artifacts to /opt/ml/model
Option B is correct because Amazon SageMaker expects the training container to save model artifacts to the `/opt/ml/model` directory, which SageMaker automatically copies to Amazon S3 after training completes. This is a required contract for any custom training container used with SageMaker.
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.
- ✗
Expose a REST API endpoint for inference
Why it's wrong here
Training containers do not need to expose endpoints.
- ✓
Implement the train function in the container that saves model artifacts to /opt/ml/model
Why this is correct
SageMaker expects the model to be saved in /opt/ml/model.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Define a Docker Compose file to manage multi-container training
Why it's wrong here
SageMaker does not use Docker Compose.
- ✓
Include a training script that reads hyperparameters from /opt/ml/input/config/hyperparameters.json
Why this is correct
SageMaker passes hyperparameters in this file.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Push the container image to Docker Hub
Why it's wrong here
SageMaker requires the image to be in Amazon ECR.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the requirements for a training container versus an inference container, thinking that exposing an API endpoint or pushing to Docker Hub is necessary for training, when SageMaker strictly enforces the `/opt/ml` directory contract and uses Amazon ECR for image storage.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker mounts the `/opt/ml` directory structure into the training container, including `/opt/ml/input/config/hyperparameters.json` for hyperparameters and `/opt/ml/model` for output artifacts. The training script must read hyperparameters from that JSON file and write the final model to `/opt/ml/model`; SageMaker then automatically uploads the contents of `/opt/ml/model` to the configured S3 output path. A common real-world mistake is hardcoding hyperparameter paths or saving models to a different directory, causing training to fail silently or artifacts to be lost.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
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.
- →
Machine Learning Implementation and Operations — study guide chapter
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement the train function in the container that saves model artifacts to /opt/ml/model — Option B is correct because Amazon SageMaker expects the training container to save model artifacts to the `/opt/ml/model` directory, which SageMaker automatically copies to Amazon S3 after training completes. This is a required contract for any custom training container used with SageMaker.
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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Last reviewed: Jul 4, 2026
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.
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