MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
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?
⚠ Common exam trap
Many exam-takers 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.
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
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.
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.
- ✗
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.
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Same concept, more angles
1 more way this is tested on MLA-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 company needs to deploy a large language model (LLM) on SageMaker with the Triton Inference Server to maximize GPU utilization and reduce latency. They have an NVIDIA A100 GPU. Which SageMaker inference option supports Triton?
hard- A.SageMaker Batch Transform with Triton
- ✓ B.SageMaker real-time endpoint using a Triton Inference Server container
- C.SageMaker Serverless Inference with a custom container
- D.SageMaker Neo compiled model on a CPU endpoint
Why B: SageMaker real-time endpoints support the Triton Inference Server through a pre-built container that integrates with NVIDIA A100 GPUs, enabling dynamic batching and concurrent model execution to maximize GPU utilization and reduce latency. Triton is designed for high-throughput inference on GPU hardware, making it the correct choice for this scenario.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.