- A
Use SageMaker Neo to compile the model for Triton
Why wrong: Neo compiles for various targets but not for Triton; Triton is a serving framework.
- B
Package Triton as a custom container and use SageMaker batch transform
Why wrong: A custom container is possible but unnecessary; batch transform is for offline, not real-time GPU inference.
- C
Use the SageMaker Triton Inference Server container from the Deep Learning Containers
The SageMaker Triton DLC is pre-configured for Triton and supports PyTorch models.
- D
Use the standard SageMaker PyTorch container and install Triton at runtime
Why wrong: Installing Triton at runtime is inefficient and not recommended; SageMaker provides an optimized container.
Question 770 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 company wants to deploy a PyTorch model on SageMaker using the NVIDIA Triton Inference Server for GPU acceleration. They have an existing Triton configuration. Which approach should they take?
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 Triton Inference Server container from the Deep Learning Containers
Option C is correct because AWS provides a pre-built SageMaker Triton Inference Server container as part of the Deep Learning Containers (DLCs), which is optimized for GPU acceleration and supports the existing Triton configuration without modification. This container integrates directly with SageMaker hosting endpoints, enabling seamless deployment of PyTorch models with Triton's features like dynamic batching and model concurrency.
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.
- ✗
Use SageMaker Neo to compile the model for Triton
Why it's wrong here
Neo compiles for various targets but not for Triton; Triton is a serving framework.
- ✗
Package Triton as a custom container and use SageMaker batch transform
Why it's wrong here
A custom container is possible but unnecessary; batch transform is for offline, not real-time GPU inference.
- ✓
Use the SageMaker Triton Inference Server container from the Deep Learning Containers
Why this is correct
The SageMaker Triton DLC is pre-configured for Triton and supports PyTorch models.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use the standard SageMaker PyTorch container and install Triton at runtime
Why it's wrong here
Installing Triton at runtime is inefficient and not recommended; SageMaker provides an optimized container.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume SageMaker Neo is a universal compilation tool for any inference server, but Neo is specifically for hardware-specific optimization and does not support Triton's runtime environment, leading them to incorrectly select Option A.
Detailed technical explanation
How to think about this question
The SageMaker Triton Inference Server container includes the Triton server binary, pre-configured with support for multiple backends (e.g., PyTorch, TensorRT, ONNX) and GPU-optimized memory management. Under the hood, it leverages NVIDIA's CUDA and TensorRT libraries to maximize throughput via concurrent model execution and dynamic batching, which is critical for production workloads with variable request sizes. A real-world scenario where this matters is deploying a large language model (LLM) that requires low-latency responses; using the pre-built container avoids the overhead of manually compiling Triton from source and ensures compatibility with SageMaker's auto-scaling and monitoring features.
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 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 Triton Inference Server container from the Deep Learning Containers — Option C is correct because AWS provides a pre-built SageMaker Triton Inference Server container as part of the Deep Learning Containers (DLCs), which is optimized for GPU acceleration and supports the existing Triton configuration without modification. This container integrates directly with SageMaker hosting endpoints, enabling seamless deployment of PyTorch models with Triton's features like dynamic batching and model concurrency.
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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