- A
Reduce the batch size in the inference code
Why wrong: Smaller batch may increase overhead.
- B
Decrease the number of model server workers
Why wrong: Fewer workers increase queuing delay.
- C
Enable SageMaker Elastic Inference
Why wrong: Elastic Inference may help but GPU instance often better for large models.
- D
Use a GPU instance type such as ml.p3.2xlarge
GPU accelerates matrix operations in PyTorch.
Question 30 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
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.
You are deploying a PyTorch model to a SageMaker endpoint. The model is large (5 GB) and the endpoint is using an ml.c5.2xlarge instance. Inference latency is higher than required. Which change would most effectively reduce latency?
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 a GPU instance type such as ml.p3.2xlarge
Option D is correct because the primary bottleneck for a large PyTorch model (5 GB) on a CPU instance (ml.c5.2xlarge) is the lack of GPU acceleration for matrix operations and tensor computations. Switching to a GPU instance like ml.p3.2xlarge (with NVIDIA V100 GPUs) offloads the heavy parallel computation to the GPU, drastically reducing per-inference latency for deep learning models, especially those with large parameter counts.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the batch size in the inference code
Why it's wrong here
Smaller batch may increase overhead.
- ✗
Decrease the number of model server workers
Why it's wrong here
Fewer workers increase queuing delay.
- ✗
Enable SageMaker Elastic Inference
Why it's wrong here
Elastic Inference may help but GPU instance often better for large models.
- ✓
Use a GPU instance type such as ml.p3.2xlarge
Why this is correct
GPU accelerates matrix operations in PyTorch.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose Elastic Inference (Option C) thinking it provides GPU-like acceleration at lower cost, but they overlook the model size limitation (max ~2 GB) and the added network latency, making it unsuitable for large models like a 5 GB PyTorch model.
Detailed technical explanation
How to think about this question
PyTorch models rely heavily on CUDA-accelerated tensor operations (e.g., torch.matmul, convolution) which are orders of magnitude faster on GPUs due to thousands of cores and high memory bandwidth. On a CPU instance like ml.c5.2xlarge (8 vCPUs, 16 GB RAM), loading a 5 GB model into memory and performing inference per request can take seconds, whereas a GPU instance like ml.p3.2xlarge (8 vCPUs, 61 GB RAM, 1 NVIDIA V100 with 16 GB GPU memory) can execute the same inference in milliseconds by keeping the model in GPU memory and leveraging cuDNN-optimized kernels. Additionally, SageMaker automatically configures the inference container to use GPU if available, so no code changes are needed.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a GPU instance type such as ml.p3.2xlarge — Option D is correct because the primary bottleneck for a large PyTorch model (5 GB) on a CPU instance (ml.c5.2xlarge) is the lack of GPU acceleration for matrix operations and tensor computations. Switching to a GPU instance like ml.p3.2xlarge (with NVIDIA V100 GPUs) offloads the heavy parallel computation to the GPU, drastically reducing per-inference latency for deep learning models, especially those with large parameter counts.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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