MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is deploying a model to an Amazon SageMaker endpoint for real-time inference. The model requires a GPU for low-latency predictions. Which instance type should be chosen?
⚠ Common exam trap
Many candidates confuse instance families (e.g., thinking compute-optimized or memory-optimized instances can substitute for GPU instances) or overlook the explicit GPU requirement stated in the question, leading them to select a CPU-based instance like ml.c5.xlarge or ml.m5.large.
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
✓
ml.g4dn.xlarge
The ml.g4dn.xlarge instance is correct because it includes an NVIDIA T4 GPU, which is required for low-latency real-time inference with deep learning models. GPU instances accelerate matrix operations and parallel processing, reducing inference latency compared to CPU-only instances.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
ml.c5.xlarge
Why it's wrong here
CPU instance, no GPU.
- ✗
ml.r5.2xlarge
Why it's wrong here
CPU instance, no GPU.
- ✓
ml.g4dn.xlarge
Why this is correct
GPU instance suitable for inference.
- ✗
ml.m5.large
Why it's wrong here
CPU instance, no GPU.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-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 MLS-C01 exam.