hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: Deploying a deep learning model for real-time…
A company is deploying a deep learning model for real-time inference using Amazon SageMaker. The model is a CPU-intensive XGBoost model that performs well with CPU. However, the team wants to minimize latency further by using hardware acceleration. They are considering Amazon Elastic Inference (EI) or moving to a GPU instance. The model is not optimized for GPU, so significant code changes would be required. Which approach is the MOST cost-effective way to reduce latency without changing the model code?
⚠ Common exam trap
Many exam-takers assume GPU instances are always the best for hardware acceleration, but the question explicitly states the model is not GPU-optimized and requires significant code changes, making Elastic Inference the only viable option that reduces latency without code modifications.
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
✓
Attach an Elastic Inference accelerator (e.g., ml.eia2.medium) to the existing CPU endpoint.
Amazon Elastic Inference (EI) allows you to attach a low-cost GPU-powered acceleration to an existing SageMaker CPU endpoint without any code changes. Since the XGBoost model is CPU-optimized and not GPU-native, EI provides hardware acceleration for the inference computation (specifically matrix operations) while keeping the model execution on the CPU, thus reducing latency without requiring model modifications.
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 a GPU instance (ml.p3.2xlarge) and optimize the model with SageMaker Neo compilation.
Why it's wrong here
GPU instance costs more and requires code changes to utilize GPU; Neo compilation may not be sufficient if the model is not GPU-friendly.
- ✓
Attach an Elastic Inference accelerator (e.g., ml.eia2.medium) to the existing CPU endpoint.
Why this is correct
Elastic Inference provides cost-effective acceleration for XGBoost and other models without code changes.
- ✗
Use SageMaker Neo to compile the model for CPU with INT8 quantization.
Why it's wrong here
Quantization may reduce latency but not as much as hardware acceleration; Neo compilation alone does not guarantee latency reduction without hardware support.
- ✗
Migrate the model to AWS Lambda with a custom runtime and use AVX instructions.
Why it's wrong here
Lambda has a 15-minute timeout and limited CPU options; not suitable for real-time inference with large models.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.