Quick Answer
The correct answer is to stop training early and use the model from epoch 10. This is because a rising validation loss paired with a plateauing or decreasing training loss is the classic signature of RNN overfitting, where the network begins to memorize noise in the training data rather than learning generalizable patterns. Early stopping directly addresses this by halting the training process at the point where validation performance peaks, preventing further degradation. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to interpret loss curves and apply regularization techniques in real-time; a common trap is choosing to add more epochs, which would worsen overfitting, or adjusting batch size, which does not directly counter the overfitting symptom. Remember the memory tip: when validation loss rises, training time dies—stop at the lowest validation loss, not the lowest training loss.
This MLS-C01 practice question tests your understanding of modeling. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 recurrent neural network (RNN) for time series forecasting. The training loss decreases steadily for the first 10 epochs, then plateaus. The validation loss starts increasing after epoch 10. What is the most appropriate action?
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.
Stop training early and use the model from epoch 10
Early stopping prevents overfitting; the model at epoch 10 generalizes better.
Continue training for more epochs
Why wrong: Validation loss is increasing, so more epochs will worsen overfitting.
Add more layers to the network
Why wrong: More layers increase capacity and may worsen overfitting.
Increase the batch size
Why wrong: Batch size affects convergence but not overfitting directly.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Stop training early and use the model from epoch 10
Option B is correct because validation loss increasing while training loss decreases indicates overfitting. Early stopping halts training before overfitting worsens. Option A (more epochs) would increase overfitting. Option C (batch size) is not directly addressing overfitting. Option D (more layers) may exacerbate overfitting.
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.
Stop training early and use the model from epoch 10
Why this is correct
Early stopping prevents overfitting; the model at epoch 10 generalizes better.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Continue training for more epochs
Why it's wrong here
Validation loss is increasing, so more epochs will worsen overfitting.
Add more layers to the network
Why it's wrong here
More layers increase capacity and may worsen overfitting.
Increase the batch size
Why it's wrong here
Batch size affects convergence but not overfitting directly.
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: Stop training early and use the model from epoch 10 — Option B is correct because validation loss increasing while training loss decreases indicates overfitting. Early stopping halts training before overfitting worsens. Option A (more epochs) would increase overfitting. Option C (batch size) is not directly addressing overfitting. Option D (more layers) may exacerbate overfitting.
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.
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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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