Match each model evaluation metric to its use case.
Drag a concept onto its matching description — or click a concept then click the description.
Measure of false positives in classification
Measure of false negatives in classification
Harmonic mean of precision and recall
Root mean squared error for regression
Cross-entropy loss for probabilistic classification
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Accuracy: Correct when classes are balanced and errors have equal cost
Accuracy is correctly matched with balanced classes and equal error cost; Precision with minimizing false positives; Recall with minimizing false negatives; F1 Score with balancing precision and recall for imbalanced classes. Option E incorrectly pairs Accuracy with 'when false positives are costly' which is actually the domain of Precision. The main trap is confusing accuracy with precision when dealing with asymmetric costs.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Accuracy: Correct when classes are balanced and errors have equal cost
Why this is correct
Accuracy measures overall correctness, suitable when class distribution is balanced and cost of false positives and false negatives is similar.
Precision: When false positives are costly (e.g., spam detection)
Why this is correct
Precision focuses on minimizing false positives, important in scenarios like spam detection where legitimate emails should not be flagged as spam.
Recall: When false negatives are costly (e.g., medical diagnosis)
Why this is correct
Recall emphasizes capturing all positive instances, critical in medical diagnosis to avoid missing a disease.
F1 Score: When needing balance between precision and recall, especially imbalanced classes
Why this is correct
F1 Score is the harmonic mean of precision and recall, useful for imbalanced datasets where both false positives and false negatives are important.
Accuracy: When false positives are costly
Why it's wrong here
Incorrect — this describes precision, not accuracy. Accuracy does not specifically address the cost of false positives.
Quick reference
| Algorithm | Key Exchange | Signatures | Equivalent Security Key | Notes |
|---|---|---|---|---|
| RSA-3072 | Yes | Yes | 128-bit | Widely deployed; slow for bulk data |
| ECDSA P-256 | No | Yes | 128-bit | Fast signatures; standard TLS certs |
| ECDH / ECDHE | Yes | No | 128-bit | Perfect forward secrecy in TLS 1.3 |
| DH / DHE | Yes | No | 128-bit (3072-bit key) | Replaced by ECDHE in modern TLS |
| Ed25519 | No | Yes | ~128-bit | SSH keys, modern PKI |
Go deeper
About these practice questions
One of 775 original PMLE practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE practice question is part of Courseiva's free Google Cloud 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 PMLE exam.