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 runs a SageMaker training job with the above configuration. The training completes but the model performance is poor. Which change to the hyperparameters is most likely to improve the model's AUC?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
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
✓
Decrease eta to 0.1
The training job uses XGBoost with default hyperparameters that likely cause overfitting or poor generalization. Decreasing eta (learning rate) to 0.1 slows down the learning process, allowing the model to converge more smoothly and reduce overfitting, which directly improves AUC on unseen data. This is a standard regularization technique in gradient boosting.
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 max_depth to 10
Why it's wrong here
Deeper trees increase model complexity and risk overfitting.
✗
Increase subsample to 1.0
Why it's wrong here
Using all samples per iteration reduces regularization and may cause overfitting.
✗
Increase num_round to 200
Why it's wrong here
More rounds with a high learning rate can lead to overfitting and poor generalization.
✓
Decrease eta to 0.1
Why this is correct
A lower learning rate improves generalization by taking smaller steps, often yielding better AUC.
Clue confirmation
The clue word "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 MLA-C01 exam often tests the misconception that increasing model complexity (depth, rounds, or sample usage) always improves performance, when in fact regularization techniques like lowering the learning rate are more effective for fixing poor AUC caused by overfitting.
Detailed technical explanation
How to think about this question
In XGBoost, eta (learning rate) shrinks the contribution of each tree; a lower eta requires more boosting rounds but often yields better generalization by preventing overfitting. The default eta is typically 0.3, which can be too aggressive for many datasets. Reducing eta to 0.1 is a common first step when model performance is poor, especially when combined with early stopping to find the optimal number of rounds.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: Decrease eta to 0.1 — The training job uses XGBoost with default hyperparameters that likely cause overfitting or poor generalization. Decreasing eta (learning rate) to 0.1 slows down the learning process, allowing the model to converge more smoothly and reduce overfitting, which directly improves AUC on unseen data. This is a standard regularization technique in gradient boosting.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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