- A
Underfitting due to insufficient model complexity
Why wrong: Underfitting would show poor performance on both training and test sets, not high training accuracy.
- B
Overfitting due to model complexity or insufficient regularization
Overfitting explains the large gap between high training accuracy and low test accuracy.
- C
Label noise in the training data
Why wrong: Label noise usually degrades training accuracy as well, not causing such a stark contrast.
- D
Data leakage from the test set into training
Why wrong: Data leakage could cause inflated performance, but typically affects both sets similarly; here training far exceeds test.
Question 199 of 1,000
Overfitting — High Training Accuracy, Low Test Accuracy
This AI Associate practice question tests your understanding of ai fundamentals. 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 data scientist trains a lead scoring model that achieves 99% accuracy on training data but only 65% accuracy on a held-out test set. 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 due to model complexity or insufficient regularization
Overfitting occurs when the model memorizes training data noise instead of learning generalizable patterns, leading to poor test performance.
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.
- ✗
Underfitting due to insufficient model complexity
Why it's wrong here
Underfitting would show poor performance on both training and test sets, not high training accuracy.
- ✓
Overfitting due to model complexity or insufficient regularization
Why this is correct
Overfitting explains the large gap between high training accuracy and low test accuracy.
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.
- ✗
Label noise in the training data
Why it's wrong here
Label noise usually degrades training accuracy as well, not causing such a stark contrast.
- ✗
Data leakage from the test set into training
Why it's wrong here
Data leakage could cause inflated performance, but typically affects both sets similarly; here training far exceeds test.
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
Similar concept trap
Data leakage could cause inflated performance, but typically affects both sets similarly; here training far exceeds test.
Command / output trap
Underfitting would show poor performance on both training and test sets, not high training accuracy.
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 practitioner preparing for the AI Associate 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 AI Associate 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.
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FAQ
Questions learners often ask
What does this AI Associate question test?
AI Fundamentals — This question tests AI Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Overfitting due to model complexity or insufficient regularization — Overfitting occurs when the model memorizes training data noise instead of learning generalizable patterns, leading to poor test performance.
What should I do if I get this AI Associate question wrong?
Identify which AI Associate 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on AI Associate
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is training a model to predict churn. The model achieves 99% accuracy on training data but only 60% on test data. Which issue is most likely occurring?
medium- A.Concept drift
- ✓ B.Overfitting
- C.Data leakage
- D.Underfitting
Why B: Overfitting: the model learns training data patterns too well, including noise, failing to generalize to new data.
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Last reviewed: Jul 4, 2026
This AI Associate practice question is part of Courseiva's free Salesforce 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 AI Associate exam.
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