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 data scientist is training a neural network for a multi-class classification problem with 100 classes. The model uses a softmax output layer and cross-entropy loss. During training, the loss decreases steadily but the accuracy on the validation set plateaus early. Which of the following is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
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.
Why wrong: Large batch size can lead to poor generalization but the loss would still decrease; validation accuracy might be lower but not necessarily plateau.
B
The model is overfitting the training data
Overfitting occurs when the model learns training data noise, causing training loss to keep decreasing while validation performance stagnates.
C
Number of epochs is too small
Why wrong: Too few epochs would show underfitting (both training and validation accuracy low), not a plateau after decrease.
D
Learning rate is too high
Why wrong: A high learning rate would cause the loss to oscillate or diverge, not steadily decrease.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The model is overfitting the training data
When the validation accuracy plateaus early while training loss continues to decrease, it indicates that the model is memorizing the training data rather than learning generalizable patterns. This is classic overfitting, where the softmax output layer produces high-confidence predictions for training samples but fails to generalize to unseen validation data, causing cross-entropy loss to drop on the training set while validation accuracy stagnates.
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.
✗
Batch size is too large
Why it's wrong here
Large batch size can lead to poor generalization but the loss would still decrease; validation accuracy might be lower but not necessarily plateau.
✓
The model is overfitting the training data
Why this is correct
Overfitting occurs when the model learns training data noise, causing training loss to keep decreasing while validation performance stagnates.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Number of epochs is too small
Why it's wrong here
Too few epochs would show underfitting (both training and validation accuracy low), not a plateau after decrease.
✗
Learning rate is too high
Why it's wrong here
A high learning rate would cause the loss to oscillate or diverge, not steadily decrease.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between overfitting and underfitting by pairing a decreasing training loss with a plateauing validation metric, tricking candidates into choosing learning rate or epoch issues when the real problem is memorization.
Trap categories for this question
Command / output trap
Too few epochs would show underfitting (both training and validation accuracy low), not a plateau after decrease.
Detailed technical explanation
How to think about this question
Overfitting occurs when the model capacity (e.g., number of parameters) is too high relative to the amount of training data, causing the network to learn noise and idiosyncrasies in the training set. In practice, techniques like dropout, L2 regularization, or early stopping are used to mitigate this; the softmax layer's probability distribution becomes overly confident on training samples, reducing cross-entropy loss but not improving validation accuracy.
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.
TExam Day Tips
→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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: The model is overfitting the training data — When the validation accuracy plateaus early while training loss continues to decrease, it indicates that the model is memorizing the training data rather than learning generalizable patterns. This is classic overfitting, where the softmax output layer produces high-confidence predictions for training samples but fails to generalize to unseen validation data, causing cross-entropy loss to drop on the training set while validation accuracy stagnates.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
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 →
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.
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.