- A
Spot instances have a fixed, lower price than on-demand.
Why wrong: Prices vary based on supply and demand.
- B
The training job must support checkpointing to save progress.
Needed to resume after interruption.
- C
Spot instances are only available for inference, not training.
Why wrong: Spot instances are available for both.
- D
The training algorithm must be fault-tolerant to handle interruptions.
Algorithms that can resume are suitable.
- E
Spot instances can be reclaimed with a two-minute notice.
Interruption notice allows graceful stop.
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 training a model using SageMaker and wants to use spot instances to reduce costs. Which THREE considerations should the scientist evaluate? (Choose THREE.)
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 training job must support checkpointing to save progress.
Option B is correct because SageMaker managed spot training requires checkpointing to save model state at regular intervals. If a spot instance is interrupted, the training job can resume from the last checkpoint rather than starting from scratch, which is essential for long-running or expensive training jobs.
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.
- ✗
Spot instances have a fixed, lower price than on-demand.
Why it's wrong here
Prices vary based on supply and demand.
- ✓
The training job must support checkpointing to save progress.
Why this is correct
Needed to resume after interruption.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Spot instances are only available for inference, not training.
Why it's wrong here
Spot instances are available for both.
- ✓
The training algorithm must be fault-tolerant to handle interruptions.
Why this is correct
Algorithms that can resume are suitable.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Spot instances can be reclaimed with a two-minute notice.
Why this is correct
Interruption notice allows graceful stop.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The MLS-C01 exam often tests the misconception that spot instances have a fixed lower price, when in reality the price is dynamic and based on a bidding model, and that spot instances are only for inference, whereas they are widely used for training to reduce costs.
Detailed technical explanation
How to think about this question
SageMaker spot training uses EC2 Spot Instances, which can be reclaimed by AWS with a two-minute termination notice (option E). The training algorithm must be fault-tolerant (option D) to handle these interruptions, typically by implementing checkpointing to save progress periodically to Amazon S3. Under the hood, SageMaker automatically captures the exit code and restarts the training job from the last checkpoint if the instance is interrupted.
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 training job must support checkpointing to save progress. — Option B is correct because SageMaker managed spot training requires checkpointing to save model state at regular intervals. If a spot instance is interrupted, the training job can resume from the last checkpoint rather than starting from scratch, which is essential for long-running or expensive training jobs.
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
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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