MLS-C01 Modeling Practice Question
A machine learning engineer is deploying a PyTorch model on SageMaker for real-time inference. The model requires GPU for low latency. Which instance type and configuration should the engineer 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 to an ml.p3.2xlarge instance with a SageMaker endpoint.
SageMaker real-time endpoints support GPU instances like ml.p3.2xlarge. Option A (ml.c5.4xlarge with batch transform) is a CPU instance and batch transform is for offline inference, not real-time. Option B (ml.m5.large with endpoint) is a CPU instance and not suitable for GPU-accelerated inference. Option D (ml.p3.2xlarge with batch transform) uses a GPU instance but batch transform is not real-time; a SageMaker endpoint is required for real-time 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.
- ✗
Deploy to an ml.c5.4xlarge instance with SageMaker batch transform.
Why it's wrong here
Batch transform is for offline inference, not real-time.
- ✗
Deploy to an ml.m5.large instance with a SageMaker model endpoint.
Why it's wrong here
ml.m5 is CPU only, no GPU.
- ✓
Deploy to an ml.p3.2xlarge instance with a SageMaker endpoint.
Why this is correct
p3 provides GPU; endpoint enables real-time inference.
- ✗
Deploy to an ml.p3.2xlarge instance with SageMaker batch transform.
Why it's wrong here
Batch transform is not real-time; endpoint is required.
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