This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 data scientist is deploying a model using Amazon SageMaker for real-time inference. The model is memory-intensive and requires a GPU. Which instance type should be selected for the endpoint?
i3.2xlarge
Why wrong: Storage optimized, no GPU.
c5.2xlarge
Why wrong: Compute optimized, no GPU.
r5.2xlarge
Why wrong: Memory optimized, no GPU.
p3.2xlarge
GPU instance suitable for memory-intensive models.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
p3.2xlarge
The p3.2xlarge instance is correct because it provides a GPU (NVIDIA Tesla V100) with high memory bandwidth, which is essential for memory-intensive deep learning models requiring GPU acceleration for real-time inference. SageMaker endpoints for GPU-based models must use instance types from the P or G families, as CPU-only instances like i3, c5, or r5 lack the parallel processing capabilities needed for efficient GPU inference.
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.
i3.2xlarge
Why it's wrong here
Storage optimized, no GPU.
c5.2xlarge
Why it's wrong here
Compute optimized, no GPU.
r5.2xlarge
Why it's wrong here
Memory optimized, no GPU.
p3.2xlarge
Why this is correct
GPU instance suitable for memory-intensive models.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Cisco often tests the distinction between CPU-optimized instance families (c5, r5, i3) and GPU-accelerated families (p3, g4dn), where candidates mistakenly assume that high RAM (r5) or high compute (c5) can substitute for a GPU, ignoring the fundamental hardware requirement for GPU-based inference.
Detailed technical explanation
Under the hood, the p3.2xlarge uses an NVIDIA Tesla V100 GPU with 16 GB of HBM2 memory and 5,120 CUDA cores, enabling parallel processing of large neural network layers. For real-time inference, SageMaker automatically manages GPU memory allocation and batching, but memory-intensive models (e.g., large transformers) may require careful tuning of the 'max_batch_size' and 'model_server_workers' to avoid out-of-memory errors. In practice, if the model exceeds GPU memory, you might need to upgrade to a p3.8xlarge or p3.16xlarge for additional GPU memory.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: p3.2xlarge — The p3.2xlarge instance is correct because it provides a GPU (NVIDIA Tesla V100) with high memory bandwidth, which is essential for memory-intensive deep learning models requiring GPU acceleration for real-time inference. SageMaker endpoints for GPU-based models must use instance types from the P or G families, as CPU-only instances like i3, c5, or r5 lack the parallel processing capabilities needed for efficient GPU inference.
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 11, 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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