The answer is a file named model.pth containing the model state dictionary. This is required because the SageMaker PyTorch inference toolkit automatically looks for a file named model.pth in the model artifacts to load the model weights during endpoint initialization; without it, the inference container cannot serve the PyTorch model for real-time inference. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker’s built-in inference contract, where the toolkit expects a specific filename by default—a common trap is assuming inference.py or requirements.txt are mandatory, but they are optional for custom preprocessing or dependencies. Remember the memory tip: “PTH is the path” to SageMaker inference—just the state dict file, no extra code required.
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 is deploying a PyTorch model on a SageMaker endpoint for real-time inference. The model is stored as a .pth file in an S3 bucket. The data scientist wants to use the SageMaker PyTorch inference toolkit. Which file is REQUIRED in the model artifacts to serve the model?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A file named model.pth containing the model state dictionary.
Option B is correct because the SageMaker PyTorch inference toolkit expects a file named model.pth in the model artifacts. Option A is wrong because inference.py is optional for custom code. Option C is wrong because requirements.txt is optional. Option D is wrong because the model can be loaded from S3.
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.
✗
A file named model.tar.gz that contains the model and any dependencies.
Why it's wrong here
The artifact must be a tar.gz; inside it, model.pth is the expected file.
✗
A file named inference.py that defines the model loading and prediction logic.
Why it's wrong here
inference.py is optional; the toolkit provides default inference if model.pth is present.
✓
A file named model.pth containing the model state dictionary.
Why this is correct
The PyTorch inference toolkit loads model.pth by default.
Related concept
Read the scenario before looking for a memorised answer.
✗
A file named requirements.txt listing the dependencies.
Why it's wrong here
requirements.txt is optional; the environment may already have PyTorch.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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: A file named model.pth containing the model state dictionary. — Option B is correct because the SageMaker PyTorch inference toolkit expects a file named model.pth in the model artifacts. Option A is wrong because inference.py is optional for custom code. Option C is wrong because requirements.txt is optional. Option D is wrong because the model can be loaded from S3.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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