Quick Answer
The answer is to try different learning rates, such as 0.001 or 0.1, because the linear learner’s convergence is directly tied to the learning rate. When the loss remains high and does not decrease, the learning rate may be too high, causing the gradient descent to oscillate around the minimum, or too low, leading to painfully slow convergence. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of hyperparameter tuning for SageMaker’s built-in algorithms, specifically how the learning rate controls the step size toward the optimal solution. A common trap is to assume more epochs will fix the issue, but without adjusting the learning rate, the model simply wastes compute cycles. Remember the memory tip: “If loss is stuck, the rate needs a tweak—go up or down, not just around.”
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 Amazon SageMaker to train a linear learner model for regression. After reviewing the training logs, the data scientist notices that the loss is not decreasing and remains high. The learning rate is set to 0.01. The data is normalized. What should the data scientist do to improve convergence?
Normalize the data again.
Why wrong: Data is already normalized.
Reduce the mini-batch size.
Why wrong: Reducing batch size may introduce noise but is less likely to be the primary fix.
Try different learning rates, such as 0.001 or 0.1.
Tuning the learning rate is a common first step to improve convergence.
Increase the number of epochs.
Why wrong: More epochs may not help if the loss is stuck due to suboptimal learning rate.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Try different learning rates, such as 0.001 or 0.1.
Option B is correct. The learning rate may be too high causing oscillation or too low causing slow convergence. Adjusting it can help. Option A is wrong because more epochs may not help if the learning rate is inappropriate. Option C is wrong because the data is already normalized. Option D is wrong because reducing batch size increases noise but may not resolve convergence issues.
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.
Normalize the data again.
Why it's wrong here
Data is already normalized.
Reduce the mini-batch size.
Why it's wrong here
Reducing batch size may introduce noise but is less likely to be the primary fix.
Try different learning rates, such as 0.001 or 0.1.
Why this is correct
Tuning the learning rate is a common first step to improve convergence.
Related concept
Read the scenario before looking for a memorised answer.
Increase the number of epochs.
Why it's wrong here
More epochs may not help if the loss is stuck due to suboptimal learning rate.
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 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
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 — study guide chapter
Learn the concepts, then practise the questions
Modeling practice questions
Targeted practice on this topic area only
All MLS-C01 questions
1,755 questions across all exam domains
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise MLS-C01 questions linked to Data Engineering.
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Practise MLS-C01 questions linked to Modeling.
Practise MLS-C01 questions linked to Exploratory Data Analysis.
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
Practise MLS-C01 questions linked to MLS-C01 scenario.
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Try different learning rates, such as 0.001 or 0.1. — Option B is correct. The learning rate may be too high causing oscillation or too low causing slow convergence. Adjusting it can help. Option A is wrong because more epochs may not help if the learning rate is inappropriate. Option C is wrong because the data is already normalized. Option D is wrong because reducing batch size increases noise but may not resolve convergence issues.
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.
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 →
Keep practising
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.