Quick Answer
The answer is to increase the L1 regularization strength. This is correct because L1 regularization (Lasso) drives many feature coefficients to exactly zero, effectively reducing the number of active features in the model. With fewer non-zero coefficients, the linear regression training time decreases significantly, as the algorithm has less computational work to perform during each iteration, while the model can maintain similar accuracy if the zeroed-out features were unimportant. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how regularization impacts both model sparsity and training efficiency—a common trap is confusing L1 with L2 regularization, which only shrinks coefficients but does not zero them out, offering minimal speed benefit. A helpful memory tip: L1 is for “Lasso” and “Leaving features out,” while L2 is for “Ridge” and “Reducing magnitude only.”
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 training a linear regression model using Amazon SageMaker's built-in Linear Learner algorithm. The dataset has 500 features and 1 million rows. After training, the model's training RMSE is 2.5 and validation RMSE is 2.6, which is acceptable. However, the scientist notices that many feature coefficients are very small but non-zero, and the model takes a long time to train. The scientist wants to reduce training time while maintaining similar accuracy. Which action should the scientist take?
Increase the mini-batch size
Why wrong: Larger batch may speed up but not as effective as sparsity.
Increase the L1 regularization strength
L1 induces sparsity, removing features and speeding training.
Switch to a neural network model
Why wrong: Neural networks are slower to train.
Increase the L2 regularization strength
Why wrong: L2 shrinks coefficients but does not zero them, so less speed gain.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Increase the L1 regularization strength
Option B (increase L1 regularization) will drive many coefficients to zero, reducing effective features and thus training time. Option A (increase L2) shrinks coefficients but doesn't zero them out, so less impact on speed. Option C (increase batch size) may speed training but could affect convergence. Option D (use a different algorithm) is unnecessary.
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.
Increase the mini-batch size
Why it's wrong here
Larger batch may speed up but not as effective as sparsity.
Increase the L1 regularization strength
Why this is correct
L1 induces sparsity, removing features and speeding training.
Related concept
Read the scenario before looking for a memorised answer.
Switch to a neural network model
Why it's wrong here
Neural networks are slower to train.
Increase the L2 regularization strength
Why it's wrong here
L2 shrinks coefficients but does not zero them, so less speed gain.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
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.
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Increase the L1 regularization strength — Option B (increase L1 regularization) will drive many coefficients to zero, reducing effective features and thus training time. Option A (increase L2) shrinks coefficients but doesn't zero them out, so less impact on speed. Option C (increase batch size) may speed training but could affect convergence. Option D (use a different algorithm) is unnecessary.
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.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 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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