- A
The time taken to train the model
Why wrong: Training time is irrelevant for inference instance selection.
- B
The number of vCPUs on the instance
Why wrong: vCPUs are less important for GPU inference.
- C
The cost per inference for the instance type
Cost is a key consideration.
- D
The AWS Region of the S3 bucket storing the model
Why wrong: Region does not affect instance type.
- E
The GPU memory available on the instance
GPU memory must fit model and input.
Question 779 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.
A data scientist is deploying a model on Amazon SageMaker. The model requires inference on images, and the data scientist wants to use a GPU instance for low latency. However, the data scientist is unsure about the instance type to choose for the endpoint. Which TWO factors should the data scientist consider when selecting the instance type? (Choose TWO.)
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
✓
The cost per inference for the instance type
Option C is correct because cost per inference directly impacts operational budget, especially with GPU instances that have higher hourly costs; data scientists must balance low latency with cost efficiency. Option E is correct because GPU memory limits the size of models and batch sizes that can be processed in a single inference call, directly affecting latency and throughput. Both factors are critical when selecting an instance type for real-time inference on SageMaker.
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.
- ✗
The time taken to train the model
Why it's wrong here
Training time is irrelevant for inference instance selection.
- ✗
The number of vCPUs on the instance
Why it's wrong here
vCPUs are less important for GPU inference.
- ✓
The cost per inference for the instance type
Why this is correct
Cost is a key consideration.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The AWS Region of the S3 bucket storing the model
Why it's wrong here
Region does not affect instance type.
- ✓
The GPU memory available on the instance
Why this is correct
GPU memory must fit model and input.
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 focus on training-related metrics (like vCPUs or training time) instead of inference-specific factors, or they mistakenly think regional proximity of the S3 bucket affects instance performance, when in fact the endpoint must be in the same Region but the Region itself does not constrain instance type selection.
Detailed technical explanation
How to think about this question
GPU instances like ml.p3.2xlarge provide NVIDIA V100 GPUs with 16 GB of GPU memory, which is essential for loading large deep learning models (e.g., ResNet-152 or YOLOv4) and processing high-resolution images without out-of-memory errors. The cost per inference is calculated as (instance hourly cost) / (inferences per hour), and choosing an instance with excessive GPU memory for a small model leads to unnecessary expense, while insufficient memory causes inference failures or excessive swapping.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: The cost per inference for the instance type — Option C is correct because cost per inference directly impacts operational budget, especially with GPU instances that have higher hourly costs; data scientists must balance low latency with cost efficiency. Option E is correct because GPU memory limits the size of models and batch sizes that can be processed in a single inference call, directly affecting latency and throughput. Both factors are critical when selecting an instance type for real-time inference on SageMaker.
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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