- A
Vanishing gradients
Why wrong: Vanishing gradients would prevent loss from decreasing at all, not cause a divergence.
- B
Incorrect learning rate scheduling
Why wrong: While learning rate issues can cause problems, the pattern described is classic overfitting.
- C
Overfitting
Overfitting causes the model to perform well on training data but poorly on validation data.
- D
Underfitting
Why wrong: Underfitting would show high loss on both sets, not an increasing validation loss.
Question 746 of 1,000
AI0-001 AI Concepts and Techniques Practice Question
This AI0-001 practice question tests your understanding of ai concepts and techniques. 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 trains a neural network for image classification. During training, the loss decreases on the training set but increases on the validation set after a few 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.
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
Overfitting occurs when the model learns the training data too well, including noise and irrelevant patterns, causing it to memorize rather than generalize. This is evidenced by the loss decreasing on the training set while increasing on the validation set after a few epochs, as the model's performance on unseen data degrades.
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.
- ✗
Vanishing gradients
Why it's wrong here
Vanishing gradients would prevent loss from decreasing at all, not cause a divergence.
- ✗
Incorrect learning rate scheduling
Why it's wrong here
While learning rate issues can cause problems, the pattern described is classic overfitting.
- ✓
Overfitting
Why this is correct
Overfitting causes the model to perform 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 sets, not an increasing validation loss.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between overfitting and underfitting by presenting a scenario where training loss decreases but validation loss increases, which candidates may confuse with a learning rate issue or gradient problem.
Trap categories for this question
Command / output trap
Underfitting would show high loss on both sets, not an increasing validation loss.
Detailed technical explanation
How to think about this question
Overfitting is often addressed with regularization techniques such as L1/L2 weight decay, dropout, or early stopping. In practice, a model with too many parameters relative to the dataset size will memorize training examples, and the validation loss curve will begin to rise while training loss continues to drop—a classic sign to halt training or apply regularization.
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 practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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 AI0-001 question test?
AI Concepts and Techniques — This question tests AI Concepts and Techniques — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Overfitting — Overfitting occurs when the model learns the training data too well, including noise and irrelevant patterns, causing it to memorize rather than generalize. This is evidenced by the loss decreasing on the training set while increasing on the validation set after a few epochs, as the model's performance on unseen data degrades.
What should I do if I get this AI0-001 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
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Last reviewed: Jul 4, 2026
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.
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