The answer is to increase MaxNumberOfTrainingJobs to 100. With only 20 training jobs, the Bayesian optimization strategy used by default in Amazon SageMaker automatic model tuning may not have sufficiently explored the hyperparameter space, leaving better configurations undiscovered. Increasing the maximum number of training jobs allows the algorithm to run more trials, improving tuning accuracy by enabling deeper exploration and exploitation of promising regions. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of how Bayesian optimization converges—it requires enough iterations to build an accurate surrogate model. A common trap is confusing parallel jobs (which speed up execution) with total exploration; adding parallel jobs does not increase the number of trials. Another trap is switching to random search, which is less efficient than Bayesian for this task. Memory tip: think “more jobs, more exploration—Bayesian needs reps to refine.”
MLA-C01 ML Model Development Practice Question
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.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
With only 20 training jobs, Bayesian optimization may not have fully explored the hyperparameter space. Increasing the maximum number of training jobs allows more exploration and increases the chance of finding better hyperparameters. Changing to random search could help but Bayesian is generally more efficient. Changing the objective to training accuracy would not improve generalization. Increasing parallel jobs does not increase total exploration.
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.
Clue confirmation
The clue word "best" in the question point toward this answer.
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
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
How to think about this question
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.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 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.
ML Model Development — This question tests ML Model Development — 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 — With only 20 training jobs, Bayesian optimization may not have fully explored the hyperparameter space. Increasing the maximum number of training jobs allows more exploration and increases the chance of finding better hyperparameters. Changing to random search could help but Bayesian is generally more efficient. Changing the objective to training accuracy would not improve generalization. Increasing parallel jobs does not increase total exploration.
What should I do if I get this MLA-C01 question wrong?
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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