The correct answer is that high bias can cause underfitting, while high variance leads to overfitting. This is because a model with high bias makes overly strong assumptions about the data, resulting in systematic errors that prevent it from capturing the underlying patterns—this is the essence of underfitting. In contrast, high variance means the model is too sensitive to the training data, fitting noise and random fluctuations, which leads to overfitting. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this bias-variance tradeoff is a foundational concept tested in questions about model performance and regularization. A common trap is confusing bias with model complexity: simple models have high bias, not complex ones, and ensemble methods like bagging reduce variance, not bias. To remember, think of bias as the model’s “blind spot” causing underfitting, and variance as its “jitteriness” causing overfitting.
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.
Which TWO of the following are true about the bias-variance tradeoff?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
High variance can cause overfitting
Option A is correct because high bias leads to underfitting. Option C is correct because high variance leads to overfitting. Option B is wrong because high bias models are not complex. Option D is wrong because simple models have high bias. Option E is wrong because ensemble methods reduce variance.
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.
✗
Ensemble methods like bagging increase variance
Why it's wrong here
Bagging reduces variance by averaging multiple models.
✗
Simple models tend to have high variance
Why it's wrong here
Simple models have high bias and low variance.
✓
High variance can cause overfitting
Why this is correct
High variance means the model is very sensitive to training data, leading to overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✓
High bias can cause underfitting
Why this is correct
High bias means the model is too simple to capture patterns, leading to underfitting.
Related concept
Read the scenario before looking for a memorised answer.
✗
High variance models are typically too simple
Why it's wrong here
High variance models are complex and overfit the data.
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: High variance can cause overfitting — Option A is correct because high bias leads to underfitting. Option C is correct because high variance leads to overfitting. Option B is wrong because high bias models are not complex. Option D is wrong because simple models have high bias. Option E is wrong because ensemble methods reduce variance.
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.
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 →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is training a linear regression model. After training, the model has a high bias and low variance. Which technique should the data scientist use to reduce bias?
easy
A.Decrease the model complexity
✓ B.Add more relevant features
C.Apply L2 regularization (Ridge)
D.Reduce the amount of training data
Why B: High bias indicates the model is underfitting the data, meaning it is too simple to capture the underlying patterns. Adding more relevant features increases model complexity, allowing it to learn more from the data and reduce bias. This directly addresses the underfitting issue without increasing variance excessively, provided the features are meaningful.
Variation 2. A data scientist is using Amazon SageMaker built-in XGBoost algorithm to train a regression model. The training job completes successfully but the model performance on the test set is poor, with high bias. Which hyperparameter adjustment is most likely to help reduce bias?
medium
✓ A.Increase the max_depth parameter.
B.Reduce the num_round parameter.
C.Increase the gamma parameter.
D.Decrease the max_depth parameter.
Why A: High bias (underfitting) can be reduced by increasing the model complexity. Increasing max_depth allows more complex trees. Decreasing max_depth would increase bias. Increasing gamma increases regularization and bias. Reducing num_round (number of trees) reduces complexity.
Last reviewed: Jun 20, 2026
Question Discussion
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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.
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