This MLS-C01 practice question tests your understanding of modeling. 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 company is using Amazon SageMaker to train a XGBoost model for predicting customer churn. The training data is stored in an S3 bucket as CSV files. The data scientist runs a hyperparameter tuning job with 50 training jobs. The tuning job completes, but the best model's accuracy on the holdout set is lower than expected. The data scientist suspects that the hyperparameter ranges are too narrow. Which corrective action is most appropriate?
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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Expand the hyperparameter ranges for key parameters such as 'max_depth', 'learning_rate', and 'subsample'
Option C is correct because the data scientist suspects the hyperparameter ranges are too narrow, which directly limits the model's ability to find an optimal configuration. Expanding ranges for key XGBoost parameters like 'max_depth', 'learning_rate', and 'subsample' allows the tuning job to explore a broader space of model complexities and regularization levels, potentially improving accuracy on the holdout set. This is the most direct fix for the stated problem, as it addresses the root cause rather than increasing job count or changing the search strategy.
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 the number of training jobs in the tuning job
Why it's wrong here
More jobs with the same narrow ranges may not yield improvement.
✗
Switch to a different algorithm like Random Forest
Why it's wrong here
Changing algorithms is not necessary; the issue is likely hyperparameter ranges.
✓
Expand the hyperparameter ranges for key parameters such as 'max_depth', 'learning_rate', and 'subsample'
Why this is correct
Wider ranges allow the tuning job to explore more of the hyperparameter space, potentially finding better configurations.
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 tuning strategy from random search to Bayesian optimization
Why it's wrong here
Bayesian optimization is efficient but still limited by the defined ranges.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'more training jobs' (Option A) with 'broader search space', failing to recognize that increasing jobs only refines sampling within existing bounds, not expands them.
Detailed technical explanation
How to think about this question
In SageMaker's hyperparameter tuning, the search space is defined by ranges for each parameter; random search samples uniformly, while Bayesian optimization uses past results to guide sampling, but both are confined to the specified bounds. For XGBoost, 'max_depth' controls tree complexity (typical range 3–10), 'learning_rate' (eta) shrinks feature weights (0.01–0.3), and 'subsample' prevents overfitting (0.5–1.0); narrow ranges like max_depth=3–5 may miss deeper trees that capture complex interactions. In practice, expanding ranges can reveal better trade-offs between bias and variance, especially when the holdout accuracy is unexpectedly low.
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: Expand the hyperparameter ranges for key parameters such as 'max_depth', 'learning_rate', and 'subsample' — Option C is correct because the data scientist suspects the hyperparameter ranges are too narrow, which directly limits the model's ability to find an optimal configuration. Expanding ranges for key XGBoost parameters like 'max_depth', 'learning_rate', and 'subsample' allows the tuning job to explore a broader space of model complexities and regularization levels, potentially improving accuracy on the holdout set. This is the most direct fix for the stated problem, as it addresses the root cause rather than increasing job count or changing the search strategy.
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". 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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