Question 175 of 835
easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: Use SageMaker to deploy a model that requires GPU…
A company wants to use SageMaker to deploy a model that requires GPU acceleration for inference but also needs to keep costs low when traffic is low. Which SageMaker feature should they use?
⚠ Common exam trap
Candidates often confuse SageMaker Managed Spot Training (cost savings for training) with inference cost optimization, or assume that GPU acceleration for inference requires a full GPU instance like ml.p3.2xlarge, overlooking Elastic Inference as a fractional GPU solution.
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
✓
SageMaker Elastic Inference
SageMaker Elastic Inference (EI) allows you to attach a fraction of a GPU to a SageMaker endpoint for inference, providing GPU acceleration at a lower cost than using a full GPU instance. This is ideal for scenarios with variable traffic because you can scale the EI accelerator independently of the instance, and pay only for the accelerator when it's used, keeping costs low during low-traffic periods.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Debugger
Why it's wrong here
Debugger is for debugging training jobs.
- ✗
SageMaker Managed Spot Training
Why it's wrong here
Spot Training is for training jobs, not inference.
- ✓
SageMaker Elastic Inference
Why this is correct
Elastic Inference attaches GPU acceleration to any SageMaker instance, reducing cost.
- ✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor is for monitoring inference quality.
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Last reviewed: Jun 24, 2026
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