- A
k-nearest neighbors
Why wrong: k-NN is not an ensemble method and does not reduce variance of decision trees.
- B
Random forest
Random forest is specifically designed to reduce variance by averaging many trees.
- C
Gradient boosting
Why wrong: Gradient boosting reduces bias more than variance; it can also reduce variance with careful tuning but is more prone to overfitting.
- D
Logistic regression
Why wrong: Logistic regression is a single model with high bias; it cannot reduce variance of decision trees.
Question 394 of 1,000
AIF-C01 AI and ML Fundamentals Practice Question
This AIF-C01 practice question tests your understanding of ai and ml fundamentals. 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 data scientist is building a model to predict customer churn. They have 10,000 samples with 20 features. After training a decision tree, they observe high variance. Which ensemble method would best reduce variance without significantly increasing bias?
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
✓
Random forest
A random forest reduces variance by training multiple decision trees on bootstrapped subsets of the data and averaging their predictions. This ensemble approach leverages the low bias of individual trees while smoothing out their high variance through aggregation, directly addressing the overfitting issue without substantially increasing bias.
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.
- ✗
k-nearest neighbors
Why it's wrong here
k-NN is not an ensemble method and does not reduce variance of decision trees.
- ✓
Random forest
Why this is correct
Random forest is specifically designed to reduce variance by averaging many trees.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Gradient boosting
Why it's wrong here
Gradient boosting reduces bias more than variance; it can also reduce variance with careful tuning but is more prone to overfitting.
- ✗
Logistic regression
Why it's wrong here
Logistic regression is a single model with high bias; it cannot reduce variance of decision trees.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the bias-variance tradeoff by presenting boosting as a variance-reduction method, but the trap here is that gradient boosting primarily reduces bias, not variance, and can actually increase variance if the learning rate and tree depth are not carefully tuned.
Detailed technical explanation
How to think about this question
Random forests introduce two sources of randomness—bootstrap sampling of data and random subset selection of features at each split—which decorrelate the individual trees. This decorrelation is critical because averaging correlated predictions reduces variance less effectively; the technique is formalized in Breiman's 2001 paper and typically uses sqrt(p) features for classification, where p is the total number of features. In practice, random forests often achieve near-optimal performance with default hyperparameters, making them a robust first choice for high-variance tree-based models.
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.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
AI and ML Fundamentals — This question tests AI and ML Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Random forest — A random forest reduces variance by training multiple decision trees on bootstrapped subsets of the data and averaging their predictions. This ensemble approach leverages the low bias of individual trees while smoothing out their high variance through aggregation, directly addressing the overfitting issue without substantially increasing bias.
What should I do if I get this AIF-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
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