- A
gamma (kernel coefficient)
gamma determines the radius of influence of support vectors.
- B
learning rate
Why wrong: Learning rate is not a hyperparameter for SVM; SVM uses quadratic programming.
- C
epsilon (for epsilon-SVR)
Why wrong: Epsilon is used in SVM regression, not classification.
- D
degree (for polynomial kernel)
Why wrong: Degree is only relevant for polynomial kernel, not RBF.
- E
C (regularization parameter)
C controls the penalty for misclassification, directly affecting margin.
Question 961 of 1,000
SVM with RBF Kernel — Hyperparameter Tuning (C and Gamma) | CompTIA AI+ Explained
This AI0-001 practice question tests your understanding of machine learning and deep learning. 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 is tuning hyperparameters for a support vector machine (SVM) with an RBF kernel. Which two hyperparameters most significantly affect model performance? (Select TWO.)
Quick Answer
The correct answer is C (regularization parameter) and gamma, as these two hyperparameters most significantly affect SVM with RBF kernel performance. C controls the trade-off between achieving a wider margin and minimizing misclassifications, where a larger C penalizes errors more heavily and can lead to overfitting, while gamma defines the influence radius of a single training example—low gamma means a broad influence, producing a smoother decision boundary, and high gamma captures finer details but risks overfitting. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of kernel-specific tuning, often appearing as a select-two item where distractors include polynomial degree (for polynomial kernels) or epsilon (for SVR). A common trap is confusing gamma with kernel coefficient for other kernels, so remember: for RBF, you only tune C and gamma. Memory tip: “C for Cost, gamma for Gaussian spread.”
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
✓
gamma (kernel coefficient)
Gamma defines the influence of a single training example, with low values meaning a far reach and high values meaning a close reach. C controls the trade-off between achieving a low error on the training data and minimizing the margin, directly impacting overfitting. Together, they are the two most critical hyperparameters for an SVM with an RBF kernel.
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.
- ✓
gamma (kernel coefficient)
Why this is correct
gamma determines the radius of influence of support vectors.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
learning rate
Why it's wrong here
Learning rate is not a hyperparameter for SVM; SVM uses quadratic programming.
- ✗
epsilon (for epsilon-SVR)
Why it's wrong here
Epsilon is used in SVM regression, not classification.
- ✗
degree (for polynomial kernel)
Why it's wrong here
Degree is only relevant for polynomial kernel, not RBF.
- ✓
C (regularization parameter)
Why this is correct
C controls the penalty for misclassification, directly affecting margin.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often mistake kernel-specific hyperparameters (e.g., degree for polynomial, gamma for RBF) for general SVM parameters, selecting options like degree or epsilon without realizing they do not apply to the RBF kernel.
Detailed technical explanation
How to think about this question
In an RBF kernel SVM, gamma controls the width of the Gaussian function: a small gamma produces a decision boundary that is nearly linear, while a large gamma can lead to extreme overfitting by making the boundary too sensitive to individual points. The C parameter acts as a penalty on misclassifications; a high C forces the model to classify all training points correctly at the risk of overfitting, while a low C allows a softer margin for better generalization. In practice, grid search or Bayesian optimization over log-scaled values of gamma and C is standard.
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
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FAQ
Questions learners often ask
What does this AI0-001 question test?
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: gamma (kernel coefficient) — Gamma defines the influence of a single training example, with low values meaning a far reach and high values meaning a close reach. C controls the trade-off between achieving a low error on the training data and minimizing the margin, directly impacting overfitting. Together, they are the two most critical hyperparameters for an SVM with an RBF kernel.
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.
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
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