Question 652 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. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 data science team needs to deploy a trained PyTorch model for real-time inference with sub-100ms latency. The model fits on a single GPU. Which SageMaker inference option is MOST cost-effective while meeting the latency requirement?
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
✓
SageMaker real-time endpoint on ml.g4dn.xlarge
SageMaker real-time endpoints provide dedicated, persistent instances that can handle synchronous inference with sub-100ms latency. The ml.g4dn.xlarge instance includes a single NVIDIA T4 GPU, which is sufficient for the model size and offers the lowest cost among GPU instances that meet the latency requirement. This option balances performance and cost for real-time, low-latency inference.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose SageMaker Serverless Inference for its cost-saving potential, but they overlook the cold start latency and lack of GPU support, which makes it unsuitable for real-time, sub-100ms inference with PyTorch models.
Detailed technical explanation
How to think about this question
SageMaker real-time endpoints use Amazon Elastic Inference or dedicated GPU instances like the g4dn series, which leverage NVIDIA T4 GPUs with Tensor Cores for mixed-precision inference. The ml.g4dn.xlarge provides 4 vCPUs, 16 GiB memory, and 1 T4 GPU, costing approximately $0.526 per hour, making it the most cost-effective GPU instance for models that fit on a single GPU. Under the hood, SageMaker deploys the model as a containerized service behind an Application Load Balancer, ensuring consistent low-latency responses for synchronous requests.
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
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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: SageMaker real-time endpoint on ml.g4dn.xlarge — SageMaker real-time endpoints provide dedicated, persistent instances that can handle synchronous inference with sub-100ms latency. The ml.g4dn.xlarge instance includes a single NVIDIA T4 GPU, which is sufficient for the model size and offers the lowest cost among GPU instances that meet the latency requirement. This option balances performance and cost for real-time, low-latency inference.
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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