A team is testing a neural network for a financial fraud detection system. They have a test set that achieves high neuron coverage, but they are concerned that the test set may not include enough adversarial examples. Which complementary testing technique would best address this concern?

Using a higher activation threshold

Increasing the number of neurons

A team is using neuron coverage to guide their testing of a neural network. They notice that adding more test inputs increases coverage, but the number of detected faults does not increase proportionally. What does this observation illustrate?

The activation threshold is too high

The weak correlation between coverage and fault detection

A team is testing a neural network and wants to measure coverage using threshold coverage. They set multiple thresholds for each neuron and check whether the test set causes activations above each threshold. What is the primary advantage of threshold coverage over neuron coverage?

It provides more granular information about activation levels

It guarantees higher fault detection

A team is testing a neural network and has achieved 100% neuron coverage. However, they are concerned that the coverage metric may not be sufficient to ensure the model's robustness. They are considering using a combination of coverage measures and mutation testing. What is the primary advantage of this combined approach?

It provides a more comprehensive assessment of test suite adequacy

It guarantees that all possible faults are detected

It reduces the number of test cases needed

It eliminates the need for domain-specific test design

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Domain 6Objective 2

Coverage Measures for Neural Networks CT-AI Practice Questions (Page 2)

Part of the Domain 6: ML - Neural Networks and Testing domain, which makes up ~2% of our current practice bank. ISTQB does not publish an official question count, but from its 60-minute exam (~25–40 total, ~1–1 in this domain), expect 1–1 from this objective — we provide 14 practice questions to prepare you well beyond it. (estimate)

14questions here

3free pages

3concepts

Questions 6–10

6foundation · easy
Which structural coverage measure for neural networks records whether each neuron's activation value exceeds a predefined threshold at least once during testing?
Select an answer first
7expert · medium
A team is testing a neural network for a financial fraud detection system. They have a test set that achieves high neuron coverage, but they are concerned that the test set may not include enough adversarial examples. Which complementary testing technique would best address this concern?
Select an answer first
8application · medium
A team is using neuron coverage to guide their testing of a neural network. They notice that adding more test inputs increases coverage, but the number of detected faults does not increase proportionally. What does this observation illustrate?
Select an answer first
9application · medium
A team is testing a neural network and wants to measure coverage using threshold coverage. They set multiple thresholds for each neuron and check whether the test set causes activations above each threshold. What is the primary advantage of threshold coverage over neuron coverage?
Select an answer first
10expert · medium
A team is testing a neural network and has achieved 100% neuron coverage. However, they are concerned that the coverage metric may not be sufficient to ensure the model's robustness. They are considering using a combination of coverage measures and mutation testing. What is the primary advantage of this combined approach?
Select an answer first

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Coverage Measures for Neural Networks CT-AI Practice Questions (Page 2)

Questions 6–10

Which structural coverage measure for neural networks records whether each neuron's activation value exceeds a predefined threshold at least once during testing?

A team is testing a neural network for a financial fraud detection system. They have a test set that achieves high neuron coverage, but they are concerned that the test set may not include enough adversarial examples. Which complementary testing technique would best address this concern?

A team is using neuron coverage to guide their testing of a neural network. They notice that adding more test inputs increases coverage, but the number of detected faults does not increase proportionally. What does this observation illustrate?

A team is testing a neural network and wants to measure coverage using threshold coverage. They set multiple thresholds for each neuron and check whether the test set causes activations above each threshold. What is the primary advantage of threshold coverage over neuron coverage?

Keep studying — Domain 6: ML - Neural Networks and Testing