The answer is to add feature crosses for categorical variables. This is correct because high bias in a linear learner signals underfitting, where the model is too simple to capture complex patterns in the data. Feature crosses create interaction terms between categorical variables, allowing the linear model to represent non-linear relationships and increase its expressiveness, directly reducing bias without switching algorithms. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of bias-variance tradeoff and feature engineering for linear models—a common trap is to assume you must use a non-linear algorithm like XGBoost, but the exam expects you to know that feature crosses are the standard fix for underfitting in linear learners. Memory tip: think of "crossing" as building bridges between categories to let a straight line bend.
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.
A data scientist is using SageMaker to train a linear learner algorithm. After training, the evaluation shows that the model has high bias. Which action is most likely to reduce bias?
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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add feature crosses for categorical variables
High bias indicates that the model is underfitting the data, meaning it is too simple to capture the underlying patterns. Adding feature crosses for categorical variables creates interaction features that allow the linear learner to model non-linear relationships, increasing model complexity and reducing bias. This is a standard technique in linear models to address underfitting without switching to a non-linear algorithm.
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.
✗
Increase the L2 regularization strength
Why it's wrong here
Increasing regularization penalizes complexity and increases bias.
✗
Reduce the amount of training data
Why it's wrong here
Less data typically leads to higher bias because the model has fewer examples to learn from.
✓
Add feature crosses for categorical variables
Why this is correct
Adding feature crosses increases model capacity to capture interactions, reducing bias.
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.
✗
Remove some features that have low variance
Why it's wrong here
Removing features reduces model complexity and increases bias.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse bias with variance and incorrectly choose regularization (Option A) to fix underfitting, when regularization actually increases bias and is used to combat overfitting (high variance).
Detailed technical explanation
How to think about this question
Feature crosses in SageMaker's linear learner are implemented by creating Cartesian products of categorical features, which are then one-hot encoded into the feature matrix. This allows the linear model to learn separate weights for each combination of categories, effectively capturing interactions that a simple linear model would miss. In practice, this is crucial for domains like click-through rate prediction, where user and ad category interactions are highly predictive.
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.
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: Add feature crosses for categorical variables — High bias indicates that the model is underfitting the data, meaning it is too simple to capture the underlying patterns. Adding feature crosses for categorical variables creates interaction features that allow the linear learner to model non-linear relationships, increasing model complexity and reducing bias. This is a standard technique in linear models to address underfitting without switching to a non-linear algorithm.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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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.
Question Discussion
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