- A
Model is overfitting; add dropout regularization.
Diverging validation loss after training loss decrease is classic overfitting; dropout helps.
- B
Training data is not representative; collect more data.
Why wrong: Data issues may cause poor validation but overfitting is more directly indicated by the loss pattern.
- C
Model is underfitting; increase model capacity.
Why wrong: Underfitting shows high training loss, not decreasing loss.
- D
Learning rate too high; reduce learning rate.
Why wrong: High learning rate often prevents convergence, not a typical cause of validation loss divergence after initial decrease.
Question 8 of 1,000
AI0-001 AI Models and Data Engineering Practice Question
This AI0-001 practice question tests your understanding of ai models and data engineering. 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 data scientist trains a deep learning model on a large dataset. The training loss decreases steadily but the validation loss starts increasing after 20 epochs. The scientist uses early stopping with patience=5. Which of the following is the MOST likely cause and best corrective action?
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
✓
Model is overfitting; add dropout regularization.
The training loss decreasing while validation loss increasing after 20 epochs is a classic sign of overfitting, where the model memorizes training data noise instead of generalizing. Early stopping with patience=5 would halt training after 5 epochs of no validation improvement, but the root cause is overfitting. Adding dropout regularization randomly drops neurons during training, forcing the network to learn more robust features and reducing overfitting.
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.
- ✓
Model is overfitting; add dropout regularization.
Why this is correct
Diverging validation loss after training loss decrease is classic overfitting; dropout helps.
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.
- ✗
Training data is not representative; collect more data.
Why it's wrong here
Data issues may cause poor validation but overfitting is more directly indicated by the loss pattern.
- ✗
Model is underfitting; increase model capacity.
Why it's wrong here
Underfitting shows high training loss, not decreasing loss.
- ✗
Learning rate too high; reduce learning rate.
Why it's wrong here
High learning rate often prevents convergence, not a typical cause of validation loss divergence after initial decrease.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the distinction between overfitting and underfitting by showing a diverging validation loss curve, and the trap here is that candidates may confuse overfitting with a learning rate issue or data quality problem, leading them to choose 'reduce learning rate' or 'collect more data' instead of the correct regularization technique.
Trap categories for this question
Command / output trap
Underfitting shows high training loss, not decreasing loss.
Detailed technical explanation
How to think about this question
Dropout works by randomly setting a fraction of neuron activations to zero during each forward pass, effectively training an ensemble of sub-networks and preventing co-adaptation of features. The patience parameter in early stopping defines the number of epochs to wait after validation loss stops improving before halting training, which is a form of regularization but does not fix the underlying overfitting. In practice, combining dropout with early stopping is common, but dropout directly addresses the overfitting mechanism by adding noise to the learning process.
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.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Model is overfitting; add dropout regularization. — The training loss decreasing while validation loss increasing after 20 epochs is a classic sign of overfitting, where the model memorizes training data noise instead of generalizing. Early stopping with patience=5 would halt training after 5 epochs of no validation improvement, but the root cause is overfitting. Adding dropout regularization randomly drops neurons during training, forcing the network to learn more robust features and reducing overfitting.
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.
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Last reviewed: Jun 30, 2026
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