Question 345 of 1,672
Spot Instances for SageMaker Training: Key Considerations
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.)
Quick Answer
Using Spot Instances for SageMaker training means trading guaranteed availability for a lower price, and the considerations that matter most all stem from that trade-off, specifically, the fact that AWS can reclaim a Spot Instance with only a two-minute interruption notice whenever the capacity is needed elsewhere or the Spot price exceeds your bid. That short notice window means a training job has to be designed to survive an abrupt interruption rather than assuming it will run uninterrupted from start to finish, which is why SageMaker managed spot training requires checkpointing: the training job periodically saves its current state, model weights, optimizer state, and progress, to persistent storage, so if an interruption occurs, SageMaker can automatically resume from the most recent checkpoint instead of restarting the entire job from scratch. Without checkpointing, an interruption partway through a long or expensive training run would waste all the progress made up to that point, which would erase much of the cost savings Spot Instances are meant to provide in the first place. Understanding this pairing, the interruption behavior on one side, and the resilience mechanism needed to tolerate it on the other, is the core concept behind using Spot Instances safely for training rather than just chasing the lower price. Whenever a question asks what to consider before using Spot Instances for training, expect the correct considerations to center on the interruption behavior, notice period, reclaim risk, and the mechanisms, like checkpointing, needed to make a job resilient to that interruption.
⚠ Common exam trap
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.
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.
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.
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.
- ✗
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.
- ✓
Spot instances can be reclaimed with a two-minute notice.
Why this is correct
Interruption notice allows graceful stop.
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Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist uses SageMaker to train a model. The training job takes 10 hours, but the team needs to reduce costs. Which approach is MOST cost-effective?
medium- ✓ A.Enable Managed Spot Training
- B.Use SageMaker Automatic Model Tuning
- C.Use a larger instance type to finish faster
- D.Use SageMaker Distributed Training with more instances
Why A: Managed Spot Training leverages Amazon EC2 Spot Instances, which offer spare compute capacity at up to 90% discount compared to On-Demand instances. This makes it the most cost-effective approach for reducing training costs. Option A is correct. Option B (using Automatic Model Tuning) is designed for hyperparameter optimization, not cost reduction, and may increase cost due to additional training jobs. Option C (using a larger instance type) finishes faster but at a higher per-hour cost, potentially increasing total cost. Option D (using Distributed Training with more instances) increases resource usage and cost, though it may reduce training time.
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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