MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is deploying a model using Amazon SageMaker for real-time inference. The model is memory-intensive and requires a GPU. Which instance type should be selected for the endpoint?
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between CPU-optimized instance families (c5, r5, i3) and GPU-accelerated families (p3, g4dn), where candidates mistakenly assume that high RAM (r5) or high compute (c5) can substitute for a GPU, ignoring the fundamental hardware requirement for GPU-based inference.
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
✓
p3.2xlarge
The p3.2xlarge instance is correct because it provides a GPU (NVIDIA Tesla V100) with high memory bandwidth, which is essential for memory-intensive deep learning models requiring GPU acceleration for real-time inference. SageMaker endpoints for GPU-based models must use instance types from the P or G families, as CPU-only instances like i3, c5, or r5 lack the parallel processing capabilities needed for efficient GPU inference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
i3.2xlarge
Why it's wrong here
Storage optimized, no GPU.
- ✗
c5.2xlarge
Why it's wrong here
Compute optimized, no GPU.
- ✗
r5.2xlarge
Why it's wrong here
Memory optimized, no GPU.
- ✓
p3.2xlarge
Why this is correct
GPU instance suitable for memory-intensive models.
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