- A
Use a warm start from a previous tuning job
Why wrong: Warm start reuses previous results but does not reduce the time of the current tuning job.
- B
Use early stopping to prune poorly performing training jobs
Early stopping kills underperforming trials early, saving time.
- C
Switch to a smaller instance type for each training job
Why wrong: Smaller instances run each trial more slowly, likely increasing overall time.
- D
Increase the number of concurrent training jobs
More concurrent jobs run in parallel, reducing wall-clock time.
- E
Reduce the number of hyperparameter combinations by using a smaller search space
A smaller search space reduces the total number of trials needed.
Question 395 of 1,000
hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: A machine learning engineer is using SageMaker's…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 engineer is using SageMaker's HyperparameterTuningJob to optimize a neural network. The engineer observes that the tuning job is taking too long. Which three actions can reduce the tuning time? (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
✓
Use early stopping to prune poorly performing training jobs
Option B is correct because SageMaker's early stopping feature automatically terminates poorly performing training jobs during the hyperparameter tuning process. By stopping jobs that are unlikely to produce optimal results based on the objective metric, it frees up resources and significantly reduces the total tuning time without sacrificing the quality of the final model.
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.
- ✗
Use a warm start from a previous tuning job
Why it's wrong here
Warm start reuses previous results but does not reduce the time of the current tuning job.
- ✓
Use early stopping to prune poorly performing training jobs
Why this is correct
Early stopping kills underperforming trials early, saving time.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch to a smaller instance type for each training job
Why it's wrong here
Smaller instances run each trial more slowly, likely increasing overall time.
- ✓
Increase the number of concurrent training jobs
Why this is correct
More concurrent jobs run in parallel, reducing wall-clock time.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Reduce the number of hyperparameter combinations by using a smaller search space
Why this is correct
A smaller search space reduces the total number of trials needed.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that reducing instance size always speeds up tuning, but in reality, smaller instances increase per-job training time and can lead to resource contention, making the overall tuning process slower.
Detailed technical explanation
How to think about this question
SageMaker's HyperparameterTuningJob uses Bayesian optimization or random search to explore the hyperparameter space. Early stopping leverages the 'objective metric' defined in the tuning job configuration; if a training job's metric does not improve over a specified number of steps (e.g., using the 'MaxRuntimeInSeconds' or 'TrainingJobEarlyStoppingType' parameter set to 'Auto'), SageMaker automatically stops it. This is particularly effective for neural networks where convergence can be slow and many hyperparameter combinations yield poor results early in training.
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 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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Use early stopping to prune poorly performing training jobs — Option B is correct because SageMaker's early stopping feature automatically terminates poorly performing training jobs during the hyperparameter tuning process. By stopping jobs that are unlikely to produce optimal results based on the objective metric, it frees up resources and significantly reduces the total tuning time without sacrificing the quality of the final model.
What should I do if I get this MLA-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 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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