The answer is to increase the static num_round hyperparameter to 500. This is the correct choice because XGBoost builds an ensemble of decision trees sequentially, and each boosting round corrects errors from the previous round; a validation RMSE of 2.34 that has not plateaued indicates the model is underfit and has not yet converged, so adding more rounds allows the algorithm to capture more complex patterns and reduce residual error. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding of how XGBoost hyperparameter tuning interacts with convergence—a common trap is to assume that more rounds always cause overfitting, but without early stopping or a plateau in validation loss, increasing num_round is the primary lever to lower RMSE. Remember the memory tip: “More rounds, less underfound”—if your validation loss is still dropping, keep boosting until it flattens.
MLS-C01 Modeling Practice Question
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.
Network Topology
A data scientist ran a hyperparameter tuning job for an XGBoost model. The tuning job completed, but the best validation RMSE is 2.34. The data scientist believes the model can perform better. Based on the exhibit, which change to the tuning strategy is most likely to improve the model's performance?
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: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the static num_round hyperparameter to 500
Option D is correct because increasing the static `num_round` hyperparameter to 500 allows the model to train for more boosting rounds, which can reduce underfitting and lower the RMSE further. The current best validation RMSE of 2.34 suggests the model may not have converged, and additional rounds can help the XGBoost model learn more complex patterns, provided overfitting is monitored with early stopping.
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 random search instead of Bayesian optimization
Why it's wrong here
Random search may explore more but not necessarily find better parameters; the limitation is likely the fixed num_round.
✗
Change the objective to binary:logistic
Why it's wrong here
The objective is reg:linear for regression; changing to binary classification would be inappropriate for a regression problem.
✗
Increase the maximum value of eta to 1.0
Why it's wrong here
A higher eta can cause overshooting; the current range 0.01-0.5 is typical; increasing max may not help.
✓
Increase the static num_round hyperparameter to 500
Why this is correct
The tuning job fixed num_round to 100; increasing it allows more boosting rounds, which can improve model performance.
Clue confirmation
The clue words "best", "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think increasing `eta` to 1.0 accelerates learning, but they overlook that a high learning rate without sufficient boosting rounds or regularization often causes the model to overshoot the optimal solution, degrading RMSE.
Detailed technical explanation
How to think about this question
In XGBoost, `num_round` controls the number of boosting iterations, and a low value can lead to underfitting, while a high value with a small learning rate often improves performance. Bayesian optimization balances exploration and exploitation by modeling the objective function with a Gaussian process, making it more sample-efficient than random search. The `eta` hyperparameter shrinks the contribution of each tree; typical values are 0.01–0.3, and setting it to 1.0 removes regularization, risking overfitting or instability.
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 static num_round hyperparameter to 500 — Option D is correct because increasing the static `num_round` hyperparameter to 500 allows the model to train for more boosting rounds, which can reduce underfitting and lower the RMSE further. The current best validation RMSE of 2.34 suggests the model may not have converged, and additional rounds can help the XGBoost model learn more complex patterns, provided overfitting is monitored with early stopping.
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", "most likely". 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
This MLS-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 MLS-C01 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.