MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company wants to serve a large ensemble of models using NVIDIA Triton Inference Server on SageMaker for high throughput GPU inference. Which SageMaker inference option supports this?
⚠ Common exam trap
MLA-C01 often tests the assumption that any SageMaker hosting option can run Triton, when only a real-time endpoint with a custom container supports arbitrary inference servers like 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
✓
Real-time endpoint with a custom container running Triton
NVIDIA Triton Inference Server is a custom inference server that supports multiple frameworks, model ensembles, and dynamic batching. To use it on SageMaker, you deploy a real-time endpoint with a custom container that runs Triton, which supports GPU inference and high throughput. This is the only option that explicitly supports Triton.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Asynchronous Inference
Why it's wrong here
Asynchronous Inference queues requests for large payloads and long processing, but it does not run NVIDIA Triton's multi-model ensemble on GPU instances. It is tempting for throughput, yet it targets near-real-time queued workloads rather than Triton model ensembles.
- ✗
Multi-model endpoint
Why it's wrong here
Multi-model endpoints load and unload models dynamically from Amazon S3 on a shared container, targeting many models with intermittent traffic rather than a persistent GPU-resident ensemble. Triton hosts multiple models concurrently on one endpoint, which is what the scenario requires for high-throughput GPU inference.
- ✗
Serverless Inference
Why it's wrong here
Serverless Inference abstracts instances entirely and does not expose GPU selection or Triton model repository configuration, so ensembles cannot be hosted. It is tempting for variable, intermittent traffic, but Triton ensemble serving requires the GPU-backed real-time endpoint option.
- ✓
Real-time endpoint with a custom container running Triton
Why this is correct
A real-time endpoint with a custom container lets you run NVIDIA Triton Inference Server directly, enabling ensemble execution, dynamic batching and concurrent model execution on GPU instances. This satisfies the high-throughput GPU inference requirement, which single-model SageMaker containers cannot provide.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLA-C01 question from scratch — 665 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.