Quick Answer
The answer is a single .tar.gz file containing the model files. SageMaker requires this specific format for custom containers because its inference pipeline extracts the archive directly into the `/opt/ml/model` directory, ensuring the container can load the model and any inference code from a predictable path. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker’s deployment mechanics versus training—a common trap is confusing the flexible input formats for training with the rigid single-archive requirement for real-time inference endpoints. Remember that SageMaker’s built-in algorithms also expect this format, but for custom containers, you must explicitly package everything, including serialized weights (e.g., model.pth) and any custom inference scripts, into that one compressed file. A quick memory tip: think “one tar to rule them all”—SageMaker extracts one archive to one directory, so your model artifact must be a single .tar.gz, not a folder or multiple files.
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 team has trained a deep learning model on Amazon SageMaker using a custom Docker container. They want to deploy the model to a SageMaker endpoint for real-time inference. Which format should the model artifacts be in?
A single .tar.gz file containing the model files.
SageMaker requires model artifacts as a tarball.
A folder on S3 with the model files.
Why wrong: SageMaker expects a tar.gz file, not a folder.
No format requirement; any file works.
Why wrong: SageMaker requires a specific format.
A .zip file containing the model files.
Why wrong: SageMaker expects .tar.gz, not .zip.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
A single .tar.gz file containing the model files.
Amazon SageMaker requires model artifacts to be packaged as a single .tar.gz file when using a custom Docker container for real-time inference. This compressed archive must contain the model files (e.g., model.pth, model.h5) and any necessary inference code, as SageMaker extracts the archive to the /opt/ml/model directory during deployment. The .tar.gz format ensures consistent extraction and compatibility with SageMaker's inference pipeline.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
A single .tar.gz file containing the model files.
Why this is correct
SageMaker requires model artifacts as a tarball.
Related concept
Read the scenario before looking for a memorised answer.
A folder on S3 with the model files.
Why it's wrong here
SageMaker expects a tar.gz file, not a folder.
No format requirement; any file works.
Why it's wrong here
SageMaker requires a specific format.
A .zip file containing the model files.
Why it's wrong here
SageMaker expects .tar.gz, not .zip.
Common exam traps
The trap here is that candidates may assume SageMaker accepts common archive formats like .zip or any file structure, but the exam specifically tests the requirement for a single .tar.gz file as the only supported format for model artifacts in custom container deployments.
Detailed technical explanation
Under the hood, SageMaker's inference container mounts the model artifacts from S3 and extracts the .tar.gz file to /opt/ml/model, where the inference code (e.g., in a custom container) loads the model using environment variables like SAGEMAKER_BATCH. A subtle behavior is that the .tar.gz must not contain a top-level directory; the model files should be at the root of the archive to avoid path issues. In real-world scenarios, forgetting to flatten the archive structure is a common cause of deployment failures.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — study guide chapter
Learn the concepts, then practise the questions
Modeling 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
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise MLS-C01 questions linked to Data Engineering.
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Practise MLS-C01 questions linked to Modeling.
Practise MLS-C01 questions linked to Exploratory Data Analysis.
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
Practise MLS-C01 questions linked to MLS-C01 scenario.
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: A single .tar.gz file containing the model files. — Amazon SageMaker requires model artifacts to be packaged as a single .tar.gz file when using a custom Docker container for real-time inference. This compressed archive must contain the model files (e.g., model.pth, model.h5) and any necessary inference code, as SageMaker extracts the archive to the /opt/ml/model directory during deployment. The .tar.gz format ensures consistent extraction and compatibility with SageMaker's inference pipeline.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 →
Last reviewed: Jun 24, 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.