Question 520 of 1,672
MLS-C01 Practice Question: Machine Learning Implementation and Operations
Which THREE are valid considerations when deploying a large deep learning model (10 GB) on a SageMaker endpoint? (Choose 3.)
⚠ Common exam trap
A common mix-up: candidates assume Serverless Inference or Elastic Inference can handle any model size, but both have hard limits (1 GB for Elastic Inference, 1 GB model size and 6 MB payload for Serverless) that make them invalid for a 10 GB model.
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
✓
Enable SageMaker Data Compression for network transfer.
SageMaker Data Compression uses HTTP compression (e.g., gzip) to reduce the payload size during network transfer between the client and endpoint, which is critical for a 10 GB model to minimize latency and bandwidth consumption. This is especially beneficial when the model is large and inference requests involve substantial input or output data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable SageMaker Data Compression for network transfer.
Why this is correct
Compression reduces data transfer time.
- ✓
Use GPU instances (e.g., p3, inf1) for faster inference.
Why this is correct
GPUs accelerate deep learning inference.
- ✓
Use SageMaker Multi-Model Endpoints to serve multiple models.
Why this is correct
Multi-model endpoints can share resources efficiently.
- ✗
Use SageMaker Serverless Inference to avoid managing instances.
Why it's wrong here
Serverless has payload limits (6 MB) and cold starts.
- ✗
Attach Elastic Inference accelerators.
Why it's wrong here
Elastic Inference is deprecated.
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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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