- A
Define a health check endpoint
Why wrong: Optional, not mandatory for container execution.
- B
Use SageMaker Batch Transform
Why wrong: Batch transform is not needed for real-time inference.
- C
Include the SageMaker inference toolkit in the container
Required for SageMaker to interface with the container.
- D
Choose a GPU instance type for the endpoint
CUDA requires GPU hardware.
- E
Set the container's entry point to the inference script
SageMaker calls the entry point for predictions.
Question 1,469 of 1,755
Required Steps for Custom Docker Containers on SageMaker Inference
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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.
You are deploying a custom Docker container for a SageMaker model that requires a specific NVIDIA CUDA version. Which THREE steps must you take to ensure the container runs correctly on SageMaker?
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
✓
Include the SageMaker inference toolkit in the container
Option C is correct because the SageMaker inference toolkit provides the necessary SageMaker-compatible HTTP server and lifecycle management (e.g., model loading, serving, and health checks) that SageMaker expects from a custom container. Without it, the container would not properly integrate with SageMaker's invocation and scaling mechanisms, even if the CUDA dependencies are correct.
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.
- ✗
Define a health check endpoint
Why it's wrong here
Optional, not mandatory for container execution.
- ✗
Use SageMaker Batch Transform
Why it's wrong here
Batch transform is not needed for real-time inference.
- ✓
Include the SageMaker inference toolkit in the container
Why this is correct
Required for SageMaker to interface with the container.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Choose a GPU instance type for the endpoint
Why this is correct
CUDA requires GPU hardware.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Set the container's entry point to the inference script
Why this is correct
SageMaker calls the entry point for predictions.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse optional best practices (like defining a custom health check) with mandatory requirements, or they mistakenly think Batch Transform is a deployment step rather than a separate inference mode, when the core requirement is integrating the container with SageMaker's inference toolkit.
Detailed technical explanation
How to think about this question
The SageMaker inference toolkit (sagemaker-inference) includes a multi-model server (MMS) or TorchServe integration that handles the /ping and /invocations endpoints, model loading, and graceful shutdown. When using a custom Docker container with a specific CUDA version, you must also ensure the container's base image matches the CUDA runtime expected by the SageMaker environment (e.g., using nvidia/cuda:11.8-runtime-ubuntu20.04) and that the NVIDIA drivers on the host are compatible, though SageMaker manages the driver layer for GPU instances.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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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: Include the SageMaker inference toolkit in the container — Option C is correct because the SageMaker inference toolkit provides the necessary SageMaker-compatible HTTP server and lifecycle management (e.g., model loading, serving, and health checks) that SageMaker expects from a custom container. Without it, the container would not properly integrate with SageMaker's invocation and scaling mechanisms, even if the CUDA dependencies are correct.
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 →
Same concept, more angles
2 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist needs to deploy a model with a custom inference container. Which THREE requirements must the container meet for SageMaker hosting?
medium- A.Provide a training script at /opt/ml/input/data
- B.Use the SageMaker Python SDK to load the model
- ✓ C.Implement a /ping endpoint for health checks
- ✓ D.Serve on port 8080
- ✓ E.Implement a /invocations endpoint for predictions
Why C: SageMaker requires custom inference containers to implement the /ping endpoint for health checks (C), serve on port 8080 (D), and implement the /invocations endpoint for predictions (E). Option A is for training containers, not inference. Option B is unnecessary; the container can load the model using any method.
Variation 2. A data scientist creates a model resource in SageMaker using the JSON configuration in the exhibit. When creating an endpoint, the deployment fails with an error 'ModelError: Cannot find inference code'. What is the MOST likely cause?
medium- A.The model.tar.gz file is missing the model weights
- B.The ECR image does not exist
- ✓ C.The inference container environment does not specify SAGEMAKER_PROGRAM
- D.The training container does not have the SAGEMAKER_PROGRAM variable
Why C: The error 'Cannot find inference code' occurs because SageMaker requires the `SAGEMAKER_PROGRAM` environment variable in the inference container to specify the entry-point script (e.g., `inference.py`) inside the `model.tar.gz`. Without this variable, SageMaker does not know which script to execute for inference, causing the deployment to fail. Option C correctly identifies this missing environment variable as the root cause.
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Last reviewed: Jul 4, 2026
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