Quick Answer
The answer is overfitting the training data, which is the most likely cause when training loss decreases but validation loss increases on Amazon SageMaker. This happens because the model is memorizing noise and specific patterns in the training set rather than learning generalizable features, so its performance degrades on unseen validation data. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of the bias-variance tradeoff and model regularization techniques, often appearing in questions about monitoring SageMaker training jobs or tuning hyperparameters. A common trap is confusing this with underfitting, where both losses would remain high, or with data leakage, which typically causes both losses to be artificially low. Remember the memory tip: “Down training, up validation? That’s overfitting’s clear indication.”
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 machine learning team is training a deep learning model on Amazon SageMaker and notices that the training loss is decreasing but the validation loss is increasing. What 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.
Vanishing gradients
Why wrong: Vanishing gradients would prevent training loss from decreasing.
Overfitting the training data
Overfitting occurs when model learns noise, causing validation loss to increase after a point.
Learning rate is too high
Why wrong: High learning rate would cause training loss to fluctuate or diverge.
Underfitting the training data
Why wrong: Underfitting would show high training loss.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Overfitting the training data
When training loss continues to decrease while validation loss increases, the model is memorizing the training data rather than learning generalizable patterns. This is the classic symptom of overfitting, where the model's capacity exceeds what is needed for the underlying data distribution, causing it to fit noise in the training set. In Amazon SageMaker, this can be observed by monitoring the validation loss metric during training jobs.
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.
Vanishing gradients
Why it's wrong here
Vanishing gradients would prevent training loss from decreasing.
Overfitting the training data
Why this is correct
Overfitting occurs when model learns noise, causing validation loss to increase after a point.
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.
Learning rate is too high
Why it's wrong here
High learning rate would cause training loss to fluctuate or diverge.
Underfitting the training data
Why it's wrong here
Underfitting would show high training loss.
Common exam traps
Cisco often tests the distinction between overfitting and high learning rate by presenting a scenario where training loss decreases but validation loss increases, and candidates mistakenly attribute it to a learning rate that is too high, not recognizing that a high learning rate would cause both losses to diverge or oscillate.
Trap categories for this question
Command / output trap
Underfitting would show high training loss.
Detailed technical explanation
Overfitting occurs when the model's capacity (e.g., number of parameters, depth of layers) exceeds the amount of training data or when regularization techniques like dropout, L2 weight decay, or early stopping are insufficient. In practice, a model with millions of parameters trained on a small dataset will often memorize individual examples, leading to near-zero training loss but poor generalization. SageMaker's built-in algorithms like XGBoost and linear learner include hyperparameters for regularization, and custom models can leverage SageMaker's Debugger to monitor gradients and loss curves in real time.
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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — study guide chapter
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Overfitting the training data — When training loss continues to decrease while validation loss increases, the model is memorizing the training data rather than learning generalizable patterns. This is the classic symptom of overfitting, where the model's capacity exceeds what is needed for the underlying data distribution, causing it to fit noise in the training set. In Amazon SageMaker, this can be observed by monitoring the validation loss metric during training jobs.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 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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