Question 1,349 of 1,672
MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning engineer is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large deep learning model that requires low latency (under 100 ms) and high throughput (1000 requests per second). Which SageMaker deployment option is MOST suitable?
⚠ Common exam trap
It's easy for candidates to assume serverless or multi-model endpoints are always cost-effective for high throughput, but they fail to account for the strict latency and concurrency ceilings that make those options unsuitable for demanding real-time inference workloads.
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 Inference Recommender to find the optimal instance type and endpoint configuration.
SageMaker Inference Recommender runs load tests against the model to identify the optimal instance type, instance count, and endpoint configuration (e.g., container parameters, model server settings) that meet the specific latency and throughput requirements. For a large deep learning model demanding under 100 ms latency and 1000 requests per second, this automated benchmarking is essential to avoid over-provisioning or under-provisioning resources.
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 the model on a single endpoint with automatic scaling based on CPU utilization.
Why it's wrong here
Auto Scaling based on CPU may not be enough to guarantee low latency; it also requires correct instance selection.
- ✗
Use SageMaker Serverless Inference with provisioned concurrency.
Why it's wrong here
Serverless Inference has a maximum concurrency of 200 per endpoint and may not achieve 1000 TPS with low latency.
- ✓
Use SageMaker Inference Recommender to find the optimal instance type and endpoint configuration.
Why this is correct
Inference Recommender runs load tests and suggests the best instance and configuration to meet latency and throughput targets.
- ✗
Use a multi-model endpoint to load multiple copies of the model on the same instance.
Why it's wrong here
Multi-model endpoints are for multiple distinct models, not for scaling a single large model efficiently.
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Last reviewed: Jul 4, 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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