This MLS-C01 practice question tests your understanding of modeling. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company is deploying a real-time inference endpoint with SageMaker. The model is a large neural network that requires GPU acceleration. Which TWO configurations must be set?
Instance type with GPU
Required for GPU inference.
Create a SageMaker model with the inference code and model artifacts
Required to deploy endpoint.
Batch transform job
Why wrong: For offline inference.
Production variant
Why wrong: Part of endpoint configuration, but not a separate configuration.
Training container image
Why wrong: Inference container is different.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Instance type with GPU
Option A is correct because deploying a real-time inference endpoint with a large neural network that requires GPU acceleration necessitates selecting an instance type with a GPU, such as the ml.p3 or ml.g4dn series, to provide the parallel processing power needed for low-latency inference. Without a GPU instance, the model would fall back to CPU, leading to unacceptable inference times for large neural networks.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Instance type with GPU
Why this is correct
Required for GPU inference.
Related concept
Read the scenario before looking for a memorised answer.
Create a SageMaker model with the inference code and model artifacts
Why this is correct
Required to deploy endpoint.
Related concept
Read the scenario before looking for a memorised answer.
Batch transform job
Why it's wrong here
For offline inference.
Production variant
Why it's wrong here
Part of endpoint configuration, but not a separate configuration.
Training container image
Why it's wrong here
Inference container is different.
Common exam traps
The trap here is that candidates often confuse the required configurations for deploying a real-time endpoint with those for training or batch processing, mistakenly selecting Batch Transform or Training Container Image instead of recognizing that the instance type with GPU and the SageMaker model definition are the two essential components.
Detailed technical explanation
Under the hood, SageMaker real-time endpoints use Amazon Elastic Inference or GPU instances to accelerate inference, with the model loaded into memory and exposed via a RESTful API using the SageMaker Inference Toolkit. A subtle behavior is that the SageMaker model object (Option B) must include both the model artifacts (e.g., model.tar.gz) and the inference code (e.g., a Docker image or a script in a source directory) to define the inference handler, which is then deployed to the GPU instance. In a real-world scenario, failing to set the GPU instance type would cause the endpoint to use a CPU instance, resulting in inference latency exceeding the required Service Level Agreement (SLA) for real-time applications like fraud detection.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Instance type with GPU — Option A is correct because deploying a real-time inference endpoint with a large neural network that requires GPU acceleration necessitates selecting an instance type with a GPU, such as the ml.p3 or ml.g4dn series, to provide the parallel processing power needed for low-latency inference. Without a GPU instance, the model would fall back to CPU, leading to unacceptable inference times for large neural networks.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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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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