Quick Answer
The answer is overfitting. This is the most likely cause because in matrix factorization for recommendation systems, a steadily decreasing training loss combined with an increasing validation loss after several epochs is the classic signature of the model memorizing the training data, including its noise, rather than learning generalizable latent factors. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to diagnose overfitting by interpreting loss curves, a common trap where candidates focus on the improving training metric and overlook the diverging validation trend. A key memory tip is to think of the “diverging curves” rule: when training and validation loss move in opposite directions after an initial drop, you are seeing overfitting in action.
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 team is building a product recommendation system using matrix factorization in Amazon SageMaker. They notice that the model's training loss decreases steadily but validation loss starts increasing after 5 epochs. 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.
Underfitting
Why wrong: Underfitting would show high loss on both.
Not enough training data
Why wrong: More data typically reduces overfitting.
Learning rate too high
Why wrong: High learning rate causes loss to bounce or diverge.
Overfitting
The model is memorizing the training data.
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
In matrix factorization for recommendation systems, a decreasing training loss with an increasing validation loss after several epochs is a classic sign of overfitting. The model is memorizing the training data (including noise) rather than learning generalizable patterns, which degrades its performance on unseen validation data.
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.
Underfitting
Why it's wrong here
Underfitting would show high loss on both.
Not enough training data
Why it's wrong here
More data typically reduces overfitting.
Learning rate too high
Why it's wrong here
High learning rate causes loss to bounce or diverge.
Overfitting
Why this is correct
The model is memorizing the training data.
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.
Common exam traps
The trap here is that candidates may confuse the symptom of overfitting (training loss decreasing, validation loss increasing) with underfitting or a learning rate issue, but the key is the divergence between the two loss curves after a period of convergence.
Trap categories for this question
Command / output trap
Underfitting would show high loss on both.
Detailed technical explanation
Matrix factorization decomposes the user-item interaction matrix into lower-dimensional latent factors. Overfitting occurs when the model learns overly complex latent representations that fit the training interactions exactly but fail to generalize to unseen user-item pairs. Regularization techniques (e.g., L2 penalty on latent factors) or early stopping are standard countermeasures; the validation loss inflection point at epoch 5 signals that training should have been halted earlier.
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Overfitting — In matrix factorization for recommendation systems, a decreasing training loss with an increasing validation loss after several epochs is a classic sign of overfitting. The model is memorizing the training data (including noise) rather than learning generalizable patterns, which degrades its performance on unseen validation data.
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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