MLS-C01 Modeling Practice Question
A company is using Amazon SageMaker to host a model for real-time inference. The model is a large ensemble of 10 XGBoost models, each 2 GB. The endpoint uses a single ml.c5.18xlarge instance. The inference latency is high (average 2 seconds). Which change would most effectively reduce latency?
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
✓
Use SageMaker Multi-Model Endpoints to serve each model independently
Serialization/deserialization of large models is a bottleneck; SageMaker Multi-Model Endpoints can reduce overhead by loading only the requested model. Option B (GPU) may not help if the bottleneck is CPU. Option C (Add more instances) helps throughput but not per-request latency. Option D (Batch Transform) is for offline inference.
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 SageMaker Multi-Model Endpoints to serve each model independently
Why this is correct
Multi-Model Endpoints reduce serialization overhead by loading models on demand.
- ✗
Switch to a GPU instance type
Why it's wrong here
XGBoost is CPU-optimized; GPU may not improve latency.
- ✗
Add more instances behind a load balancer
Why it's wrong here
More instances increase throughput but not per-request latency if the model size is the bottleneck.
- ✗
Use SageMaker Batch Transform instead of real-time endpoint
Why it's wrong here
Batch Transform is not real-time and doesn't reduce latency for individual requests.
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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.