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 engineer is tuning a gradient boosting model using SageMaker Hyperparameter Tuning. The objective is to minimize MAE. The tuning job uses 20 training jobs. After 10 jobs, the best objective value is 5.2. Which action should the engineer take 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.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the total number of training jobs to 50.
Option C is correct because increasing the total number of training jobs from 20 to 50 gives the Bayesian optimization algorithm more opportunities to explore the hyperparameter space and exploit promising regions. With only 10 jobs completed, the tuning job may not have converged to the global minimum of MAE, and additional jobs can refine the search, especially since Bayesian search builds a probabilistic model that improves with more observations.
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.
✗
Set early stopping to avoid overfitting.
Why it's wrong here
Early stopping prevents overfitting but doesn't necessarily improve the best objective value.
✗
Change the objective metric to RMSE.
Why it's wrong here
Changing the metric changes the problem, not necessarily improving MAE.
✓
Increase the total number of training jobs to 50.
Why this is correct
More jobs allow broader exploration and may find a better configuration.
Clue confirmation
The clue words "best", "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Switch the tuning strategy from Bayesian to Random search.
Why it's wrong here
Random search is less efficient than Bayesian for hyperparameter tuning.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates mistakenly think early stopping (Option A) applies to the tuning job itself rather than to individual training jobs, or they assume changing the metric (Option B) will indirectly improve MAE, when in fact the tuning job's objective must directly match the business metric.
Detailed technical explanation
How to think about this question
SageMaker Hyperparameter Tuning with Bayesian optimization uses a Gaussian process regression model to predict the objective metric for untried hyperparameter combinations, balancing exploration and exploitation. The expected improvement acquisition function guides the selection of the next hyperparameter set, and with only 10 of 20 jobs completed, the surrogate model may still have high uncertainty, so increasing the total jobs to 50 allows the model to converge more reliably. In practice, the optimal number of tuning jobs often scales with the number of hyperparameters and their ranges, and a common heuristic is to use at least 10–20 times the number of hyperparameters.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the total number of training jobs to 50. — Option C is correct because increasing the total number of training jobs from 20 to 50 gives the Bayesian optimization algorithm more opportunities to explore the hyperparameter space and exploit promising regions. With only 10 jobs completed, the tuning job may not have converged to the global minimum of MAE, and additional jobs can refine the search, especially since Bayesian search builds a probabilistic model that improves with more observations.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best", "minimum / minimize". 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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Question Discussion
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