Quick Answer
The answer is early_stopping_rounds. This hyperparameter tells the SageMaker built-in LightGBM algorithm to halt training if the validation metric does not improve for a specified number of consecutive boosting rounds, directly preventing overfitting by stopping before the model begins to memorize noise. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your ability to distinguish between LightGBM’s actual early stopping parameter and common distractors like num_iterations (which sets the maximum tree count) or the invalid early_stopping. A frequent trap is confusing early_stopping_rounds with num_boost_round, but remember: the former controls when to stop early, the latter controls the total number of rounds allowed. For the exam, just think “rounds without improvement” to lock in early_stopping_rounds as the correct choice.
This MLA-C01 practice question tests your understanding of ml model development. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 engineer is using SageMaker to train a model with the built-in LightGBM algorithm. The engineer wants to use early stopping to prevent overfitting. The training job is configured with a validation dataset. Which hyperparameter should be set to enable early stopping?
early_stopping_rounds
early_stopping_rounds triggers early stopping after a specified number of rounds without validation improvement.
num_iterations
Why wrong: num_iterations sets the maximum number of iterations, not early stopping.
early_stopping
Why wrong: early_stopping is not a valid hyperparameter for LightGBM in SageMaker.
num_boost_round
Why wrong: num_boost_round sets the number of boosting rounds, not early stopping.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
early_stopping_rounds
Option B is correct because the SageMaker LightGBM implementation uses the early_stopping_rounds hyperparameter to specify the number of consecutive rounds without improvement before stopping. Option A (num_iterations) sets the maximum number of rounds. Option C (num_boost_round) is an alias for the number of boosting rounds. Option D (early_stopping) is not a valid hyperparameter name.
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.
early_stopping_rounds
Why this is correct
early_stopping_rounds triggers early stopping after a specified number of rounds without validation improvement.
Related concept
Read the scenario before looking for a memorised answer.
num_iterations
Why it's wrong here
num_iterations sets the maximum number of iterations, not early stopping.
early_stopping
Why it's wrong here
early_stopping is not a valid hyperparameter for LightGBM in SageMaker.
num_boost_round
Why it's wrong here
num_boost_round sets the number of boosting rounds, not early stopping.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
The correct answer is: early_stopping_rounds — Option B is correct because the SageMaker LightGBM implementation uses the early_stopping_rounds hyperparameter to specify the number of consecutive rounds without improvement before stopping. Option A (num_iterations) sets the maximum number of rounds. Option C (num_boost_round) is an alias for the number of boosting rounds. Option D (early_stopping) is not a valid hyperparameter name.
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 23, 2026
This MLA-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 MLA-C01 exam.
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