- A
subsample
Why wrong: Subsample reduces row sampling but has a smaller effect on memory per tree.
- B
num_round
Why wrong: num_round controls the number of trees, not the per-tree memory.
- C
max_depth
Reducing max_depth decreases tree depth and memory usage.
- D
colsample_bytree
Why wrong: Reducing colsample_bytree decreases feature usage per tree but memory savings are often less significant than max_depth.
Question 73 of 1,000
easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A machine learning engineer is training a model…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 training a model using SageMaker's built-in XGBoost algorithm. The training job fails with an error indicating insufficient memory. Which parameter should be adjusted to reduce memory usage?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
max_depth
Option C (max_depth) is correct because reducing the maximum depth of trees directly limits the number of splits and nodes per tree, which decreases the memory required to store the tree structure during training. In XGBoost, deeper trees exponentially increase the number of leaf nodes and intermediate splits, consuming more RAM for gradient statistics and tree data. Adjusting max_depth is the most direct way to reduce per-tree memory footprint without altering the dataset size or number of trees.
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.
- ✗
subsample
Why it's wrong here
Subsample reduces row sampling but has a smaller effect on memory per tree.
- ✗
num_round
Why it's wrong here
num_round controls the number of trees, not the per-tree memory.
- ✓
max_depth
Why this is correct
Reducing max_depth decreases tree depth and memory usage.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
colsample_bytree
Why it's wrong here
Reducing colsample_bytree decreases feature usage per tree but memory savings are often less significant than max_depth.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS exams often test the misconception that subsample or colsample_bytree are the primary knobs for memory reduction, when in fact max_depth has the most direct impact on per-tree memory consumption due to exponential node growth.
Detailed technical explanation
How to think about this question
XGBoost allocates memory for each node's gradient statistics (sum of gradients and hessians) and for the tree structure itself; the number of nodes grows as 2^(max_depth+1)-1, so reducing max_depth from 10 to 6 cuts potential nodes from 2047 to 127, drastically lowering memory. In practice, memory errors often occur when training on high-cardinality categorical features or large datasets with deep trees, and tuning max_depth is the first step before resorting to distributed training or instance upgrades.
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: max_depth — Option C (max_depth) is correct because reducing the maximum depth of trees directly limits the number of splits and nodes per tree, which decreases the memory required to store the tree structure during training. In XGBoost, deeper trees exponentially increase the number of leaf nodes and intermediate splits, consuming more RAM for gradient statistics and tree data. Adjusting max_depth is the most direct way to reduce per-tree memory footprint without altering the dataset size or number of trees.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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