- A
A file named model.tar.gz that contains the model and any dependencies.
Why wrong: The artifact must be a tar.gz; inside it, model.pth is the expected file.
- B
A file named inference.py that defines the model loading and prediction logic.
Why wrong: inference.py is optional; the toolkit provides default inference if model.pth is present.
- C
A file named model.pth containing the model state dictionary.
The PyTorch inference toolkit loads model.pth by default.
- D
A file named requirements.txt listing the dependencies.
Why wrong: requirements.txt is optional; the environment may already have PyTorch.
Question 1,078 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. 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 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
✓
A file named model.pth containing the model state dictionary.
Option C is correct because the SageMaker PyTorch inference toolkit expects the model artifact to be a single file named model.pth containing the model's state dictionary. When using the default inference handler, the toolkit automatically loads this file into the PyTorch model for serving. No additional inference script is required if the default behavior is sufficient.
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
The trap here is that candidates often assume a custom inference script (inference.py) is always required, but the SageMaker PyTorch inference toolkit provides a default handler that works with a simple model.pth file, making inference.py optional for basic use cases.
Detailed technical explanation
How to think about this question
Under the hood, the SageMaker PyTorch inference toolkit uses a default model loading function that calls torch.load('model.pth') and then wraps the model for inference. This behavior is defined in the sagemaker-pytorch-inference container's default handler code. In a real-world scenario, if you need to load a model with custom architecture or preprocessing, you must provide an inference.py, but for a standard model.pth file, the default handler suffices.
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
Learn the concepts, then practise the questions
- →
Machine Learning Implementation and Operations practice questions
Targeted practice on this topic area only
- →
All MLS-C01 questions
1,755 questions across all exam domains
- →
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLS-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Engineering practice questions
Practise MLS-C01 questions linked to Data Engineering.
Machine Learning Implementation and Operations practice questions
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Modeling practice questions
Practise MLS-C01 questions linked to Modeling.
Exploratory Data Analysis practice questions
Practise MLS-C01 questions linked to Exploratory Data Analysis.
MLS-C01 fundamentals practice questions
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
MLS-C01 scenario practice questions
Practise MLS-C01 questions linked to MLS-C01 scenario.
MLS-C01 troubleshooting practice questions
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Start a free MLS-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: A file named model.pth containing the model state dictionary. — Option C is correct because the SageMaker PyTorch inference toolkit expects the model artifact to be a single file named model.pth containing the model's state dictionary. When using the default inference handler, the toolkit automatically loads this file into the PyTorch model for serving. No additional inference script is required if the default behavior is sufficient.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More MLS-C01 practice questions
- A company needs to transfer 10 TB of data from an on-premises data center to Amazon S3. The network bandwidth is limited…
- A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Lambda fu…
- A team is building a data pipeline to process terabytes of log data daily using Amazon EMR. The data arrives in 5-minute…
- A data science team is building a real-time fraud detection system. Transactions are streamed via Amazon Kinesis Data St…
- A company uses Amazon SageMaker to train and deploy machine learning models. The training data is stored in Amazon S3 (P…
- A data engineering team is designing a data lake on AWS for machine learning workloads. The data includes structured, se…
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.