This MLS-C01 practice question tests your understanding of modeling. 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 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?
Decrease the model complexity
Why wrong: Decreasing complexity increases bias.
Add more relevant features
Adding features increases model complexity and can reduce bias.
Apply L2 regularization (Ridge)
Why wrong: L2 regularization adds penalty and increases bias, not reduces it.
Reduce the amount of training data
Why wrong: Reducing training data typically increases bias and variance.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Add more relevant features
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Decrease the model complexity
Why it's wrong here
Decreasing complexity increases bias.
Add more relevant features
Why this is correct
Adding features increases model complexity and can reduce bias.
Related concept
Read the scenario before looking for a memorised answer.
Apply L2 regularization (Ridge)
Why it's wrong here
L2 regularization adds penalty and increases bias, not reduces it.
Reduce the amount of training data
Why it's wrong here
Reducing training data typically increases bias and variance.
Common exam traps
Cisco often tests the bias-variance tradeoff by presenting regularization as a solution for high bias, but candidates must remember that regularization (L1/L2) primarily reduces variance, not bias, and can actually increase bias if applied too strongly.
Detailed technical explanation
Bias-variance tradeoff is central here: high bias often stems from a model with too few degrees of freedom (e.g., linear regression with only one feature). Adding relevant features increases the model's capacity to fit the training data, reducing bias, but must be done carefully to avoid overfitting. In practice, feature engineering (e.g., polynomial features or interaction terms) or using a more complex algorithm (e.g., decision trees) can also reduce bias, but adding relevant features is the most direct approach for linear regression.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Add more relevant features — 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.
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.
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Last reviewed: Jun 24, 2026
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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