MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company wants to use SageMaker to deploy a model that requires GPU acceleration for inference but wants to minimize costs by using a smaller attached GPU. Which options can they use? (Select TWO.)
⚠ Common exam trap
Many candidates confuse SageMaker Neo compilation (a model optimization technique) with hardware acceleration, or mistakenly think SageMaker serverless inference supports GPU, when in fact it only supports CPU-based compute.
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
✓
Amazon Elastic Inference
Amazon Elastic Inference (Option A) allows you to attach a smaller, configurable GPU acceleration resource to a SageMaker endpoint, enabling GPU-accelerated inference without the cost of a full GPU instance. This directly meets the requirement of minimizing costs by using a smaller attached GPU.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon Elastic Inference
Why this is correct
Elastic Inference attaches a GPU accelerator to a CPU instance, providing GPU acceleration at lower cost.
- ✗
SageMaker Neo compilation
Why it's wrong here
Neo optimizes model for hardware but does not provide GPU acceleration.
- ✓
Use a smaller GPU instance like ml.g4dn.xlarge instead of ml.p3.2xlarge
Why this is correct
Choosing a smaller GPU instance can reduce cost while still providing GPU acceleration.
- ✗
Quantize the model to INT8 precision
Why it's wrong here
Quantization reduces model size and speeds up inference but does not add GPU acceleration.
- ✗
Use SageMaker serverless inference with GPU
Why it's wrong here
Serverless inference does not support attached GPUs; it uses CPU only.
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This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.