The answer is overfitting, because when training loss continues to drop while validation loss rises after a few epochs, the model has memorized noise and patterns specific to the training set rather than generalizing to unseen data. This classic divergence in loss curves is the hallmark of overfitting: the model’s performance on training data improves, but its ability to predict on validation data degrades. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your ability to interpret training metrics in SageMaker and distinguish overfitting from underfitting (where both losses remain high) or a high learning rate (which typically causes erratic divergence, not a steady validation increase). A common trap is confusing overfitting with data imbalance, but imbalance usually depresses both training and validation metrics equally. Memory tip: think of the loss curves as a “V” shape—training goes down, validation goes up—that’s the overfitting signature.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. 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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Overfitting
Option A is correct because overfitting occurs when the model performs well on training data but poorly on validation data. Option B is wrong because underfitting would show high loss on both datasets. Option C is wrong because a high learning rate may cause divergence but not necessarily validation loss increase. Option D is wrong because data imbalance typically affects both training and validation metrics.
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
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.
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
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.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 MLA-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.
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Overfitting — Option A is correct because overfitting occurs when the model performs well on training data but poorly on validation data. Option B is wrong because underfitting would show high loss on both datasets. Option C is wrong because a high learning rate may cause divergence but not necessarily validation loss increase. Option D is wrong because data imbalance typically affects both training and validation metrics.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-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.
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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Question Discussion
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