The answer is to reduce max_depth to a lower value. This resolves the numeric overflow because XGBoost’s tree-based algorithm sums gradient statistics at each node; when trees grow too deep, the accumulated leaf weights can exceed the floating-point precision limit, causing an overflow error during training in SageMaker. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how hyperparameters directly affect numerical stability in gradient boosting—a common trap is confusing convergence issues (fixed by eta) with overflow issues (fixed by depth). A useful memory tip is “deep trees, deep trouble”—if you see an overflow error, always think depth first, not learning rate or rounds.
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.
Exhibit
Refer to the exhibit.
```
{
"name": "my-training-job",
"hyperParameters": {
"max_depth": "10",
"eta": "0.1",
"subsample": "0.8",
"colsample_bytree": "0.8",
"num_round": "100"
},
"trainingJobStatus": "Failed",
"failureReason": "AlgorithmError: /opt/program/src/sagemaker_xgboost_container/algorithm_mode/...
Message: The pipeline has been stopped. There was a numeric overflow in the tree."
}
A data scientist ran an XGBoost training job in SageMaker and it failed with the error shown in the exhibit. Which hyperparameter change is most likely to resolve the numeric overflow?
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.
Refer to the exhibit.
```
{
"name": "my-training-job",
"hyperParameters": {
"max_depth": "10",
"eta": "0.1",
"subsample": "0.8",
"colsample_bytree": "0.8",
"num_round": "100"
},
"trainingJobStatus": "Failed",
"failureReason": "AlgorithmError: /opt/program/src/sagemaker_xgboost_container/algorithm_mode/...
Message: The pipeline has been stopped. There was a numeric overflow in the tree."
}
A
Reduce max_depth to a lower value
Numeric overflow often occurs when trees are too deep, leading to large leaf weights. Reducing depth prevents this.
B
Increase subsample
Why wrong: Subsample ratio doesn't directly affect tree depth or leaf weights.
C
Increase eta (learning rate)
Why wrong: Increasing eta makes steps larger, potentially causing more overflow.
D
Increase num_round
Why wrong: More rounds can increase leaf values, exacerbating overflow.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Reduce max_depth to a lower value
Option A is correct because reducing max_depth prevents trees from growing too deep, which can cause numeric overflow. Option B is wrong because increasing eta helps convergence but not overflow. Option C is wrong because increasing num_round may worsen overflow. Option D is wrong because subsample doesn't affect depth.
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.
✓
Reduce max_depth to a lower value
Why this is correct
Numeric overflow often occurs when trees are too deep, leading to large leaf weights. Reducing depth prevents this.
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.
✗
Increase subsample
Why it's wrong here
Subsample ratio doesn't directly affect tree depth or leaf weights.
✗
Increase eta (learning rate)
Why it's wrong here
Increasing eta makes steps larger, potentially causing more overflow.
✗
Increase num_round
Why it's wrong here
More rounds can increase leaf values, exacerbating overflow.
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 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 MLS-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.
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: Reduce max_depth to a lower value — Option A is correct because reducing max_depth prevents trees from growing too deep, which can cause numeric overflow. Option B is wrong because increasing eta helps convergence but not overflow. Option C is wrong because increasing num_round may worsen overflow. Option D is wrong because subsample doesn't affect depth.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-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: "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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