This DA0-001 practice question tests your understanding of analyzing and modeling data. 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 analyst trains a complex model that achieves 99% accuracy on training data but only 65% on new data. 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.
Underfitting
Why wrong: Underfitting would show low accuracy on both training and test data.
Overfitting
The model performs well on training but poorly on test data, a classic sign of overfitting.
Multicollinearity
Why wrong: Multicollinearity affects interpretation but does not directly cause poor generalization.
High bias
Why wrong: High bias leads to underfitting, not a large gap between training and test accuracy.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Overfitting
The model performs exceptionally well on training data (99% accuracy) but poorly on new data (65% accuracy), which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training data rather than generalizing to unseen data, often due to excessive complexity (e.g., too many parameters or deep layers). This results in high variance and poor performance on validation or test sets.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Underfitting
Why it's wrong here
Underfitting would show low accuracy on both training and test data.
Overfitting
Why this is correct
The model performs well on training but poorly on test data, a classic sign of overfitting.
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.
Multicollinearity
Why it's wrong here
Multicollinearity affects interpretation but does not directly cause poor generalization.
High bias
Why it's wrong here
High bias leads to underfitting, not a large gap between training and test accuracy.
Common exam traps
CompTIA often tests the distinction between overfitting and underfitting by presenting a large gap between training and test accuracy, tempting candidates to choose high bias or multicollinearity due to confusion about bias-variance tradeoff or correlation issues.
Trap categories for this question
Command / output trap
Underfitting would show low accuracy on both training and test data.
Detailed technical explanation
Overfitting is often mitigated by regularization techniques (e.g., L1/L2 regularization, dropout in neural networks) or by reducing model complexity via pruning or early stopping. In practice, a model with 99% training accuracy but 65% test accuracy indicates a variance problem, where the model has memorized training examples rather than learning generalizable features. Real-world scenarios like fraud detection or medical diagnosis require careful cross-validation to avoid overfitting, as it can lead to catastrophic failures on new data.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
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FAQ
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
The correct answer is: Overfitting — The model performs exceptionally well on training data (99% accuracy) but poorly on new data (65% accuracy), which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training data rather than generalizing to unseen data, often due to excessive complexity (e.g., too many parameters or deep layers). This results in high variance and poor performance on validation or test sets.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This DA0-001 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 DA0-001 exam.
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