Question 39 of 835
hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: A machine learning engineer is deploying a custom…
A machine learning engineer is deploying a custom PyTorch model to a SageMaker endpoint for real-time inference. The model requires GPU acceleration. The engineer wants to minimize latency and cost. Which THREE actions should the engineer take? (Select THREE.)
⚠ Common exam trap
AWS often tests the distinction between real-time vs. batch inference and the trade-off between full GPU instances and lighter acceleration options like Elastic Inference, expecting candidates to recognize that Batch Transform is not suitable for low-latency endpoints and that CPU-only instances cannot meet GPU requirements.
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
✓
Compile the model with SageMaker Neo
SageMaker Neo compiles the PyTorch model into an optimized runtime binary that is specifically tuned for the target hardware (e.g., GPU instances like ml.p3). This reduces inference latency by applying graph-level optimizations, operator fusion, and memory layout transformations without changing the model's accuracy, while also lowering compute resource usage and cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use an ml.c5.2xlarge instance with CPU only
Why it's wrong here
CPU only would not provide GPU acceleration required.
- ✗
Use SageMaker Batch Transform for inference
Why it's wrong here
Batch Transform is for offline inference, not real-time.
- ✓
Compile the model with SageMaker Neo
Why this is correct
Neo optimizes the model for faster inference on target hardware.
- ✓
Use SageMaker Elastic Inference (EI) instead of a full GPU instance
Why this is correct
EI provides GPU acceleration at lower cost for small models.
- ✓
Use an ml.p3.2xlarge instance for the endpoint
Why this is correct
GPU instance provides needed acceleration for low latency.
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Last reviewed: Jun 30, 2026
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