This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 deploys a PyTorch model on Amazon SageMaker for real-time inference. The model requires GPU for low latency. Which instance type is MOST cost-effective while meeting the GPU requirement?
ml.m5.2xlarge
Why wrong: ml.m5.2xlarge is a general-purpose instance without GPU.
ml.p4d.24xlarge
Why wrong: ml.p4d.24xlarge is a high-end GPU instance that is more expensive than needed.
ml.p3.2xlarge
ml.p3.2xlarge provides a GPU at a cost-effective price point.
ml.c5.2xlarge
Why wrong: ml.c5.2xlarge is compute-optimized but does not provide GPU.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
ml.p3.2xlarge
Option C (ml.p3.2xlarge) is correct because it provides a GPU (NVIDIA V100) necessary for low-latency PyTorch inference on SageMaker, while being the most cost-effective among GPU options. The ml.p3.2xlarge offers a single GPU with sufficient compute for many real-time inference workloads, avoiding the higher cost of larger instances like ml.p4d.24xlarge.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
ml.m5.2xlarge
Why it's wrong here
ml.m5.2xlarge is a general-purpose instance without GPU.
ml.p4d.24xlarge
Why it's wrong here
ml.p4d.24xlarge is a high-end GPU instance that is more expensive than needed.
ml.p3.2xlarge
Why this is correct
ml.p3.2xlarge provides a GPU at a cost-effective price point.
Related concept
Read the scenario before looking for a memorised answer.
ml.c5.2xlarge
Why it's wrong here
ml.c5.2xlarge is compute-optimized but does not provide GPU.
Common exam traps
The trap here is that candidates may assume any GPU instance is equally cost-effective, overlooking that ml.p4d.24xlarge is overprovisioned for typical inference, while CPU-only instances like ml.m5 and ml.c5 are tempting but fail the explicit GPU requirement.
Detailed technical explanation
Under the hood, PyTorch models leverage CUDA kernels for GPU acceleration; the ml.p3.2xlarge uses an NVIDIA V100 with 16 GB GPU memory, which is often sufficient for models like BERT-base or ResNet-50 in real-time inference. In contrast, ml.p4d.24xlarge instances are designed for training or large-scale inference with multiple A100 GPUs, leading to unnecessary cost for single-model endpoints. SageMaker real-time endpoints automatically load-balance across instances, so choosing a smaller GPU instance can still meet latency SLAs while minimizing spend.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
The correct answer is: ml.p3.2xlarge — Option C (ml.p3.2xlarge) is correct because it provides a GPU (NVIDIA V100) necessary for low-latency PyTorch inference on SageMaker, while being the most cost-effective among GPU options. The ml.p3.2xlarge offers a single GPU with sufficient compute for many real-time inference workloads, avoiding the higher cost of larger instances like ml.p4d.24xlarge.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 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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