- A
Learning rate too high
Why wrong: A high learning rate may cause loss oscillation or divergence, not specifically increased validation loss.
- B
Overfitting
Correct: Overfitting occurs when the model performs well on training data but poorly on validation data.
- C
Underfitting
Why wrong: Underfitting would show high loss on both datasets, not just validation.
- D
Data imbalance
Why wrong: Data imbalance typically affects overall accuracy but not the pattern of training vs validation loss.
Question 510 of 1,000
easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: Training a binary classifier in SageMaker and…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 company is training a binary classifier in SageMaker and observes that the training loss decreases but validation loss increases after a few epochs. What is the most likely issue?
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.
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
✓
Overfitting
The training loss decreasing while validation loss increasing after a few epochs is the classic signature of overfitting. The model is memorizing the training data (including noise) rather than learning generalizable patterns, which causes it to perform poorly on unseen validation data. In SageMaker, this often occurs when the model has too many parameters relative to the dataset size, or when regularization techniques like dropout or L2 weight decay are insufficient.
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.
- ✗
Learning rate too high
Why it's wrong here
A high learning rate may cause loss oscillation or divergence, not specifically increased validation loss.
- ✓
Overfitting
Why this is correct
Correct: Overfitting occurs when the model performs well on training data but poorly on validation 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.
- ✗
Underfitting
Why it's wrong here
Underfitting would show high loss on both datasets, not just validation.
- ✗
Data imbalance
Why it's wrong here
Data imbalance typically affects overall accuracy but not the pattern of training vs validation loss.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often mistake the pattern of decreasing training loss with increasing validation loss for a high learning rate, but a high learning rate would typically cause both losses to oscillate or diverge, not show this specific pattern.
Trap categories for this question
Command / output trap
Underfitting would show high loss on both datasets, not just validation.
Detailed technical explanation
How to think about this question
Overfitting occurs when the model's capacity (e.g., number of parameters, tree depth) exceeds what is necessary for the underlying data distribution, causing it to fit the training set's idiosyncrasies. In SageMaker, using built-in algorithms like XGBoost, this can be mitigated by tuning hyperparameters such as `subsample`, `eta`, `max_depth`, or `gamma`, or by adding early stopping based on validation loss. A real-world scenario is training a deep neural network on a small dataset without dropout or data augmentation, where the validation loss plateaus or rises after a few epochs while training loss continues to drop.
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 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
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Overfitting — The training loss decreasing while validation loss increasing after a few epochs is the classic signature of overfitting. The model is memorizing the training data (including noise) rather than learning generalizable patterns, which causes it to perform poorly on unseen validation data. In SageMaker, this often occurs when the model has too many parameters relative to the dataset size, or when regularization techniques like dropout or L2 weight decay are insufficient.
What should I do if I get this MLA-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.
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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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