Question 131 of 835
easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data science team deploys a PyTorch model on…
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?
⚠ Common exam trap
Many exam-takers 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.
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
✓
ml.p3.2xlarge
(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.
Answer analysis
Option-by-option breakdown
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.
- ✗
ml.c5.2xlarge
Why it's wrong here
ml.c5.2xlarge is compute-optimized but does not provide GPU.
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Last reviewed: Jun 24, 2026
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