- A
Increase the mini-batch size
Why wrong: Increasing mini-batch size may speed up training by processing more data per batch, but it can affect convergence and does not address the small coefficient issue.
- B
Increase the L1 regularization strength
Increasing L1 regularization drives many coefficients to zero, reducing the effective number of features and speeding up training, while maintaining similar accuracy.
- C
Switch to a neural network model
Why wrong: Switching to a neural network is unnecessary as the linear regression model already performs well.
- D
Increase the L2 regularization strength
Why wrong: Increasing L2 regularization shrinks coefficients but does not zero them out completely, so it has less impact on training speed compared to L1.
Question 889 of 1,755
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 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?
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
✓
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 mini-batch size) may speed training but could affect convergence. Option C (switch to a neural network model) is unnecessary for this task. Option D (increase L2 regularization) shrinks coefficients but doesn't zero them out, so less impact on training speed.
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 mini-batch size
Why it's wrong here
Increasing mini-batch size may speed up training by processing more data per batch, but it can affect convergence and does not address the small coefficient issue.
- ✓
Increase the L1 regularization strength
Why this is correct
Increasing L1 regularization drives many coefficients to zero, reducing the effective number of features and speeding up training, while maintaining similar accuracy.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch to a neural network model
Why it's wrong here
Switching to a neural network is unnecessary as the linear regression model already performs well.
- ✗
Increase the L2 regularization strength
Why it's wrong here
Increasing L2 regularization shrinks coefficients but does not zero them out completely, so it has less impact on training speed compared to L1.
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 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
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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: 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 mini-batch size) may speed training but could affect convergence. Option C (switch to a neural network model) is unnecessary for this task. Option D (increase L2 regularization) shrinks coefficients but doesn't zero them out, so less impact on training speed.
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.
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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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