- A
Create a custom container from scratch with Triton and deploy on SageMaker
Why wrong: While possible, it is easier to use the pre-built container; the question asks for the required configuration, and the pre-built container is the standard approach.
- B
Use the SageMaker pre-built Triton Inference Server container available in Amazon ECR
SageMaker provides a pre-built container with Triton, ready for deployment.
- C
Use a Multi-Model Endpoint with Triton
Why wrong: MME does not support Triton natively; Triton is typically deployed on a real-time endpoint.
- D
Attach an Amazon Elastic Inference accelerator to the endpoint
Why wrong: Elastic Inference is not compatible with Triton; Triton manages its own GPU resources.
Question 897 of 1,000
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. 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.
A team needs to deploy a PyTorch model that uses custom CUDA kernels. They want to use NVIDIA Triton Inference Server on SageMaker for high-performance serving. Which SageMaker configuration is required to use Triton?
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
✓
Use the SageMaker pre-built Triton Inference Server container available in Amazon ECR
Option B is correct because SageMaker provides a pre-built Triton Inference Server container in Amazon ECR that is optimized for high-performance serving of models, including those with custom CUDA kernels. This container eliminates the need to build a custom image from scratch, ensuring compatibility with SageMaker's deployment infrastructure and reducing operational overhead.
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.
- ✗
Create a custom container from scratch with Triton and deploy on SageMaker
Why it's wrong here
While possible, it is easier to use the pre-built container; the question asks for the required configuration, and the pre-built container is the standard approach.
- ✓
Use the SageMaker pre-built Triton Inference Server container available in Amazon ECR
Why this is correct
SageMaker provides a pre-built container with Triton, ready for deployment.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a Multi-Model Endpoint with Triton
Why it's wrong here
MME does not support Triton natively; Triton is typically deployed on a real-time endpoint.
- ✗
Attach an Amazon Elastic Inference accelerator to the endpoint
Why it's wrong here
Elastic Inference is not compatible with Triton; Triton manages its own GPU resources.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that custom containers are always required for custom code, but the trap here is that SageMaker's pre-built Triton container fully supports custom CUDA kernels, making option A a redundant and incorrect choice.
Detailed technical explanation
How to think about this question
Under the hood, the SageMaker Triton container uses the NVIDIA Triton Inference Server binary, which leverages CUDA and TensorRT for GPU acceleration, and it supports custom CUDA kernels via the 'backend' API. A subtle behavior is that the container must be configured with the correct 'SAGEMAKER_TRITON_DEFAULT_MODEL_NAME' environment variable and the model repository structure (e.g., 'model_repository/model_name/1/model.pt') to avoid deployment failures. In real-world scenarios, teams often miss setting the 'SAGEMAKER_TRITON_DEFAULT_MODEL_NAME' or fail to include the custom CUDA kernel's shared library in the model artifacts, leading to runtime errors.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the SageMaker pre-built Triton Inference Server container available in Amazon ECR — Option B is correct because SageMaker provides a pre-built Triton Inference Server container in Amazon ECR that is optimized for high-performance serving of models, including those with custom CUDA kernels. This container eliminates the need to build a custom image from scratch, ensuring compatibility with SageMaker's deployment infrastructure and reducing operational overhead.
What should I do if I get this MLA-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 MLA-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 MLA-C01 exam.
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