- A
Deploy on an ml.g4dn.xlarge instance using the SageMaker Triton Inference Server container
The ml.g4dn instance has NVIDIA GPU, and the Triton container is optimized for high throughput inference.
- B
Deploy on an ml.c5.2xlarge instance using a PyTorch container
Why wrong: ml.c5 instances are CPU-only; Triton can run on CPU but GPU acceleration is desired.
- C
Deploy on an ml.p3.2xlarge instance using the SageMaker built-in XGBoost container
Why wrong: The XGBoost container does not support Triton; you need the Triton container.
- D
Deploy on an ml.m5.large instance with a standard TensorFlow serving container
Why wrong: ml.m5 instances do not have GPU support, and the standard TensorFlow serving container does not provide Triton's performance optimizations.
Question 526 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 has a large deep learning model that needs to be deployed for real-time inference with GPU acceleration. They want to use the Triton Inference Server on SageMaker to maximize throughput. Which instance type and configuration should they choose?
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
✓
Deploy on an ml.g4dn.xlarge instance using the SageMaker Triton Inference Server container
Option A is correct because the Triton Inference Server is specifically designed for high-performance inference on large deep learning models, supporting GPU acceleration and dynamic batching to maximize throughput. The ml.g4dn.xlarge instance provides a cost-effective GPU (T4) with sufficient memory for many models, and SageMaker's pre-built Triton container enables seamless deployment with features like model concurrency and request scheduling.
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.
- ✓
Deploy on an ml.g4dn.xlarge instance using the SageMaker Triton Inference Server container
Why this is correct
The ml.g4dn instance has NVIDIA GPU, and the Triton container is optimized for high throughput inference.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Deploy on an ml.c5.2xlarge instance using a PyTorch container
Why it's wrong here
ml.c5 instances are CPU-only; Triton can run on CPU but GPU acceleration is desired.
- ✗
Deploy on an ml.p3.2xlarge instance using the SageMaker built-in XGBoost container
Why it's wrong here
The XGBoost container does not support Triton; you need the Triton container.
- ✗
Deploy on an ml.m5.large instance with a standard TensorFlow serving container
Why it's wrong here
ml.m5 instances do not have GPU support, and the standard TensorFlow serving container does not provide Triton's performance optimizations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that any GPU instance (like ml.p3) is suitable for deep learning inference, but the key is matching the container (Triton) to the workload, not just the instance type, and avoiding CPU-only instances for GPU-accelerated tasks.
Detailed technical explanation
How to think about this question
The Triton Inference Server supports multiple frameworks (TensorFlow, PyTorch, ONNX) and can serve multiple models concurrently on the same GPU, using a scheduler that dynamically batches requests to optimize GPU utilization. In real-world scenarios, teams often pair Triton with NVIDIA's TensorRT for model optimization, reducing latency by up to 40% compared to standard serving, and the ml.g4dn instance's T4 GPU includes Tensor Cores for mixed-precision inference.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: Deploy on an ml.g4dn.xlarge instance using the SageMaker Triton Inference Server container — Option A is correct because the Triton Inference Server is specifically designed for high-performance inference on large deep learning models, supporting GPU acceleration and dynamic batching to maximize throughput. The ml.g4dn.xlarge instance provides a cost-effective GPU (T4) with sufficient memory for many models, and SageMaker's pre-built Triton container enables seamless deployment with features like model concurrency and request scheduling.
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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