Question 912 of 1,672
MLS-C01 Modeling Practice Question
A company is deploying a real-time inference endpoint using SageMaker. The model is a large deep learning model (5 GB) with strict latency requirements (< 100 ms per request). The team expects bursty traffic with up to 1000 requests per second. Which configuration best meets the latency and throughput requirements?
⚠ Common exam trap
Candidates often assume a single large instance (like ml.p3.16xlarge) can handle high throughput, but they overlook the need for horizontal scaling to manage bursty traffic without latency degradation.
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
✓
Deploy an ml.p3.2xlarge instance with automatic scaling based on a custom metric like 'InvocationsPerInstance'
Deploying on an ml.p3.2xlarge instance with automatic scaling based on 'InvocationsPerInstance' allows the endpoint to handle bursty traffic up to 1000 requests per second while maintaining sub-100 ms latency. The GPU-accelerated p3 instance provides the necessary compute for a 5 GB deep learning model, and custom scaling on invocations per instance ensures that additional instances are provisioned quickly during traffic spikes without over-provisioning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Deploy an ml.p3.2xlarge instance with automatic scaling based on a custom metric like 'InvocationsPerInstance'
Why this is correct
GPU instances handle large models; automatic scaling with custom metrics provides elasticity.
- ✗
Use a multi-model endpoint with ml.c5.4xlarge instances
Why it's wrong here
Multi-model endpoints are for many small models, not a single large model.
- ✗
Use SageMaker Serverless Inference with a memory size of 6 GB
Why it's wrong here
Serverless inference has cold start latency and memory limit may not be sufficient for a 5 GB model.
- ✗
Deploy a single ml.p3.16xlarge instance with a production variant
Why it's wrong here
A single instance is a single point of failure and may not handle bursty traffic well.
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Last reviewed: Jun 24, 2026
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.
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