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NEW QUESTION # 83
While measuring the test coverage of a neural network, a test engineer wants to measure the number of neurons that have each output two activation function results with a minimum difference between the two results of 0.5. Which ONE of the below coverage measures would achieve that goal?
Answer: B
Explanation:
The correct answer is B. Value change coverage . The CT-AI syllabus defines value-change coverage as requiring test cases to cause each neuron to achieve two activation values , where the difference between the two values exceeds a chosen value.
The question states that the test engineer wants to count neurons that have produced two activation function results with a minimum difference of 0.5 . This is precisely the value-change coverage concept: the selected threshold is 0.5, and a neuron is covered when its activation values vary by at least that threshold. Option A, neuron coverage, only requires neurons to achieve an activation value greater than zero; it does not assess the magnitude of change between two outputs. Option C, sign-change coverage, requires neurons to achieve both positive and negative activation values, which focuses on sign rather than numerical distance. Option D is incorrect because the syllabus explicitly includes a coverage criterion matching the described objective.
References/topics: CT-AI Syllabus Chapter 6, Section 6.2 "Coverage Measures for Neural Networks."
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NEW QUESTION # 84
Which of the following options BEST describes an example of reinforcement learning?
Answer: D
Explanation:
Reinforcement learning involves an agent learning to make decisions by taking actions that maximize a reward over time. In the example of the house cleaning robot, the robot adjusts its actions (path taken) based on feedback (efficiency and amount of dirt removed), which is characteristic of reinforcement learning.
NEW QUESTION # 85
Which performance metric is BEST suited to assess the quality of trained models detecting fraudulent credit card transactions?
Choose ONE option (1 out of 4)
Answer: A
Explanation:
The ISTQB CT-AI syllabus explains in Section3.2 - Functional Performance Criteria of ML Modelsthat accuracy becomes unreliable whenclass imbalanceexists. In fraud detection, more than 99% of transactions are non-fraudulent, meaning the dataset is extremely imbalanced. Because accuracy counts all correct non- fraudulent classifications, it will appear artificially high, even if the fraud detection performance is poor.
Therefore, accuracy is not suitable for evaluating fraud detection systems.
The syllabus further explains thatsensitivity (recall)captures the proportion of correctly identified fraudulent cases. This metric is important, as missing fraudulent events can cause high financial loss. However, the client also stresses thatlegitimate transactions must be correctly identified, meaningfalse positives must be minimizedto maintain customer satisfaction.
TheF1 score, defined as the harmonic mean ofprecision and recall, balances both:
* Precision protects legitimate customers by minimizing false alarms.
* Recall ensures fraudulent transactions are detected.
Section 3.2 emphasizes that when both false positives and false negatives have significant consequences, and the data is highly imbalanced,F1 is the most appropriate metricbecause it reflects the combined importance of detecting fraud while avoiding unnecessary alerts. Thus,Option Cis the correct choice.
NEW QUESTION # 86
An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?
Answer: C
Explanation:
Bias in AI models often originates fromincomplete or non-representative training data. In this case, if the training datasetpurposely excludes specific faulty conditions, the machine learning model willfail to learn and detectthese conditions in real-world scenarios.
This results in:
* Sample bias, where the training data is not fully representative of all possible faulty conditions.
* Algorithmic bias, where the model prioritizes certain defect types while ignoring others.
* B. Selecting training data by purposely including all known faulty conditions# This would help reduce bias by improving model generalization.
* C. Selecting testing data from a different dataset than the training dataset# This is a good practice to evaluate model generalization but does not inherently introduce bias.
* D. Selecting testing data from a boat manufacturer's bolt longevity data# While using unrelated data can createpoor model accuracy, it does not directly introduce bias unless systematic patterns in the incorrect dataset lead to unfair decision-making.
* Section 8.3 - Testing for Algorithmic, Sample, and Inappropriate Biasstates thatsample bias can occur if the training dataset is not fully representative of the expected data space, leading to biased predictions.
Why are the other options incorrect?Reference from ISTQB Certified Tester AI Testing Study Guide:
NEW QUESTION # 87
Which of the following is an example of an input change where it would be expected that the AI system should be able to adapt?
Answer: D
Explanation:
AI systems, particularly machine learning models, need to exhibit adaptability and flexibility to handle slight variations in input data without requiring retraining. The ISTQB CT-AI syllabus outlines adaptability as a crucial feature of AI systems, especially when the system is exposed to variations in its operational environment.
* Option A:"It has been trained to recognize cats and is given an image of a dog."
* This scenario introduces an entirely new class (dogs), which is outside the AI system's expected scope. If the AI was only trained to recognize cats, it would not be expected to recognize dogs correctly without retraining. This does not demonstrate adaptability as expected from an AI system.
* Option B:"It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution."
* This is an example of an AI system encountering a variation of its training data rather than entirely new data. Most AI-based image processing models can adapt to different resolutions by applying downsampling or other pre-processing techniques. Since the data remains within the domain of human faces, the model should be able to process the higher-resolution image without significant issues.
* Option C:"It has been trained to analyze mathematical models and is given a set of landscape pictures to classify."
* This represents a complete shift in the data type from structured numerical data to unstructured image data. The AI system is unlikely to adapt effectively, as it has not been trained on image classification tasks.
* Option D:"It has been trained to analyze customer buying trend data and is given information on supplier cost data."
* This introduces a significant domain shift. Customer buying trends focus on consumer behavior, while supplier cost data relates to pricing structures and logistics. The AI system would likely require retraining to process the new data meaningfully.
* Adaptability Requirements:The syllabus discusses that AI-based systems must be able to adapt to changes in their operational environment and constraints, including minor variations in input quality (such as resolution changes).
* Autonomous Learning & Evolution:AI systems are expected to improve and handle evolving inputs based on prior experience.
* Challenges in Testing Self-Learning Systems:AI systems should be tested to ensure they function correctly when encountering new but related data, such as different resolutions of the same object.
Analysis of the Answer Options:ISTQB CT-AI Syllabus References:Thus,option Bis the best choice as it aligns with the adaptability characteristics expected from AI-based systems.
NEW QUESTION # 88
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