Quick Answer
The answer is to enable early stopping to terminate poorly performing jobs. This reduces total tuning time by automatically halting training runs that show little improvement in the objective metric, freeing compute resources for more promising hyperparameter combinations. In a random search strategy, many trials naturally plateau or converge slowly, so early stopping directly cuts wasted compute without sacrificing the chance of finding an optimal configuration. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of SageMaker’s built-in hyperparameter tuning optimizations, often appearing in scenario questions where long training times are the bottleneck. A common trap is confusing early stopping with reducing the number of max training jobs or lowering resource allocation, which can degrade model quality. Remember the mnemonic: Stop the stragglers, save the budget.
This MLS-C01 practice question tests your understanding of modeling. 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 machine learning team is using Amazon SageMaker to tune hyperparameters for a neural network. They have defined a hyperparameter tuning job with a random search strategy. The training time per job is very long. Which strategy can reduce the total tuning time?
Enable early stopping to terminate poorly performing jobs.
Early stops poor trials early, saving compute time.
Use a larger instance type for each training job.
Why wrong: Faster training per job but does not reduce number of jobs.
Switch to Bayesian optimization.
Why wrong: Bayesian optimization may converge faster but still requires many jobs.
Increase the number of training jobs.
Why wrong: More jobs increase total time.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Enable early stopping to terminate poorly performing jobs.
Enabling early stopping allows SageMaker to terminate training jobs that are unlikely to produce better results based on the objective metric, which directly reduces total tuning time by freeing up compute resources for more promising hyperparameter combinations. This is especially effective with random search, where many trials may converge slowly or plateau.
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.
Enable early stopping to terminate poorly performing jobs.
Why this is correct
Early stops poor trials early, saving compute time.
Related concept
Read the scenario before looking for a memorised answer.
Use a larger instance type for each training job.
Why it's wrong here
Faster training per job but does not reduce number of jobs.
Switch to Bayesian optimization.
Why it's wrong here
Bayesian optimization may converge faster but still requires many jobs.
Increase the number of training jobs.
Why it's wrong here
More jobs increase total time.
Common exam traps
The trap here is that candidates often confuse early stopping with reducing training time per job (Option B) or assume Bayesian optimization always converges faster, but in practice, early stopping directly cuts wasted time on poor trials, which is the most effective strategy when individual training jobs are very long.
Detailed technical explanation
SageMaker's early stopping feature uses the 'MaxRuntimeInSeconds' and 'StoppingCondition' parameters, and works by monitoring the objective metric at each epoch; if the metric does not improve by a defined threshold (e.g., 5%) over a certain number of steps, the job is terminated. This is particularly useful in random search where the search space is large and many hyperparameter combinations are suboptimal, allowing the tuning job to allocate resources to more promising trials without waiting for full convergence.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Enable early stopping to terminate poorly performing jobs. — Enabling early stopping allows SageMaker to terminate training jobs that are unlikely to produce better results based on the objective metric, which directly reduces total tuning time by freeing up compute resources for more promising hyperparameter combinations. This is especially effective with random search, where many trials may converge slowly or plateau.
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 24, 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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