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Question 481 of 1,755

Modeling →mediumMultiple ChoiceObjective-mapped

Training Loss Plateau — How to Fix with Learning Rate Tuning

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 team is training a large language model using SageMaker's distributed training. They notice that the training loss is not decreasing after the first few epochs. Which action is MOST likely to resolve this issue?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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.

A
Increase the batch size
Why wrong: Increasing batch size can stabilize training by reducing gradient noise, but it does not directly resolve a plateau caused by a learning rate that is too high. It may even lead to convergence to sharp minima.
B
Add L2 regularization
Why wrong: L2 regularization adds a penalty on large weights to prevent overfitting. However, if the loss is not decreasing, the issue is likely with the optimization dynamics (e.g., learning rate), not overfitting.
C
Reduce the learning rate
A high learning rate can cause the loss to plateau or oscillate. Reducing the learning rate allows the optimizer to take smaller steps, enabling the loss to continue decreasing. This is the most direct fix.
D
Switch from Adam to SGD optimizer
Why wrong: Adam is an adaptive optimizer that adjusts learning rates per parameter. Switching to SGD may require careful tuning of the learning rate and momentum, and typically does not resolve a plateau caused by a learning rate that is too high.

Full breakdown with real-world context →

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

✓

Reduce the learning rate

When training loss plateaus or does not decrease after the first few epochs, a common cause is that the learning rate is too high, causing the optimizer to overshoot minima. Reducing the learning rate helps the model converge. Increasing the batch size (option A) mainly affects gradient variance and training speed but does not address an overly large step size. Adding L2 regularization (option B) helps prevent overfitting but does not resolve a high learning rate. Switching from Adam to SGD (option D) may not help because Adam typically adapts learning rates per parameter; if the base learning rate is too high, both optimizers can struggle. Therefore, reducing the learning rate is the most direct and effective action.

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 batch size
Why it's wrong here
Increasing batch size can stabilize training by reducing gradient noise, but it does not directly resolve a plateau caused by a learning rate that is too high. It may even lead to convergence to sharp minima.
✗
Add L2 regularization
Why it's wrong here
L2 regularization adds a penalty on large weights to prevent overfitting. However, if the loss is not decreasing, the issue is likely with the optimization dynamics (e.g., learning rate), not overfitting.
✓
Reduce the learning rate
Why this is correct
A high learning rate can cause the loss to plateau or oscillate. Reducing the learning rate allows the optimizer to take smaller steps, enabling the loss to continue decreasing. This is the most direct fix.
Clue confirmation
The clue words "first", "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Switch from Adam to SGD optimizer
Why it's wrong here
Adam is an adaptive optimizer that adjusts learning rates per parameter. Switching to SGD may require careful tuning of the learning rate and momentum, and typically does not resolve a plateau caused by a learning rate that is too high.

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 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.

Visual reference

What to study next

Got this wrong? Here's your next step.

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

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: Reduce the learning rate — When training loss plateaus or does not decrease after the first few epochs, a common cause is that the learning rate is too high, causing the optimizer to overshoot minima. Reducing the learning rate helps the model converge. Increasing the batch size (option A) mainly affects gradient variance and training speed but does not address an overly large step size. Adding L2 regularization (option B) helps prevent overfitting but does not resolve a high learning rate. Switching from Adam to SGD (option D) may not help because Adam typically adapts learning rates per parameter; if the base learning rate is too high, both optimizers can struggle. Therefore, reducing the learning rate is the most direct and effective action.

What should I do if I get this MLS-C01 question wrong?

Are there clue words in this question I should notice?

Yes — watch for: "first", "most likely". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.

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.