MLS-C01 Practice Question: Machine Learning Implementation and Operations
An ML team deploys a real-time inference endpoint on Amazon SageMaker. Users report high latency. The model is a PyTorch model using a custom container. Which combination of changes should the team implement to reduce latency? (Choose the best answer.)
⚠ Common exam trap
Watch out — candidates often assume compilation (Neo) or hardware acceleration (Elastic Inference) always reduces latency, but the question's context of high latency from a custom container on a real-time endpoint points to a misconfiguration or instance mismatch that only benchmarking can diagnose.
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 benchmark different instance families and select the best.
SageMaker Inference Recommender runs load tests across multiple instance families and configurations, providing a benchmark that identifies the optimal instance type and model server settings to minimize latency for a given model and payload. This data-driven approach directly addresses the high-latency issue without requiring code changes or switching to a different inference paradigm.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to asynchronous inference endpoint.
Why it's wrong here
Asynchronous inference is for near-real-time applications with larger payloads, not low-latency.
- ✗
Use SageMaker Elastic Inference to attach an accelerator.
Why it's wrong here
Elastic Inference is deprecated and not recommended for new deployments.
- ✗
Compile the model using SageMaker Neo.
Why it's wrong here
Neo compilation can reduce latency, but without optimal instance selection, gains may be limited.
- ✓
Use SageMaker Inference Recommender to benchmark different instance families and select the best.
Why this is correct
Inference Recommender automates benchmarking to find the optimal configuration for low latency.
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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.