This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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.
Refer to the exhibit. A data scientist configured an automatic model tuning job for a classification model. The tuning job completed after 20 training jobs, but the best validation accuracy was only 0.65. What is the most effective way to potentially improve the result?
More training jobs allow Bayesian optimization to explore more hyperparameter combinations, potentially finding a better optimum.
B
Change the strategy to Random
Why wrong: Random search may be less efficient than Bayesian; increasing the number of jobs with Bayesian is more likely to improve results.
C
Change the objective metric to training:accuracy
Why wrong: Training accuracy is not a good proxy for generalization; validation accuracy is the right metric.
D
Increase MaxParallelTrainingJobs to 10
Why wrong: Increasing parallelism does not increase the total number of trials; it speeds up execution but may reduce exploration due to concurrent jobs sharing the same early exploration.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase MaxNumberOfTrainingJobs to 100
Increasing MaxNumberOfTrainingJobs to 100 allows the automatic model tuning job to explore a larger hyperparameter space, giving the Bayesian optimization strategy (the default) more trials to converge on a better configuration. With only 20 training jobs, the tuner may not have had enough iterations to balance exploration and exploitation, especially for a complex classification model. More jobs increase the likelihood of finding a hyperparameter combination that yields higher validation accuracy.
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.
✓
Increase MaxNumberOfTrainingJobs to 100
Why this is correct
More training jobs allow Bayesian optimization to explore more hyperparameter combinations, potentially finding a better optimum.
Related concept
Read the scenario before looking for a memorised answer.
✗
Change the strategy to Random
Why it's wrong here
Random search may be less efficient than Bayesian; increasing the number of jobs with Bayesian is more likely to improve results.
✗
Change the objective metric to training:accuracy
Why it's wrong here
Training accuracy is not a good proxy for generalization; validation accuracy is the right metric.
✗
Increase MaxParallelTrainingJobs to 10
Why it's wrong here
Increasing parallelism does not increase the total number of trials; it speeds up execution but may reduce exploration due to concurrent jobs sharing the same early exploration.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that increasing parallelism (MaxParallelTrainingJobs) improves model quality, when in fact it only speeds up execution without increasing the total number of trials, which is the key lever for better hyperparameter optimization.
Detailed technical explanation
How to think about this question
Amazon SageMaker automatic model tuning uses Bayesian optimization by default, which models the objective metric as a Gaussian process and selects hyperparameters based on an acquisition function (e.g., Expected Improvement). With too few training jobs, the surrogate model may remain uncertain, leading to suboptimal recommendations. Increasing the total number of jobs allows the tuner to refine its posterior distribution and discover better regions of the hyperparameter space, especially when the search space is large or the objective landscape is rugged.
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.
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Increase MaxNumberOfTrainingJobs to 100 — Increasing MaxNumberOfTrainingJobs to 100 allows the automatic model tuning job to explore a larger hyperparameter space, giving the Bayesian optimization strategy (the default) more trials to converge on a better configuration. With only 20 training jobs, the tuner may not have had enough iterations to balance exploration and exploitation, especially for a complex classification model. More jobs increase the likelihood of finding a hyperparameter combination that yields higher validation accuracy.
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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Question Discussion
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