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NEW QUESTION # 37
Which of the following options BEST describes an example of reinforcement learning?
Answer: B
Explanation:
The correct answer is A . Reinforcement learning is described in the CT-AI syllabus as an approach in which an intelligent agent learns by interacting with its environment iteratively and learning from experience. The agent receives rewards for correct decisions and penalties for incorrect decisions. The syllabus also identifies robotics, autonomous vehicles, and chatbots as application examples for reinforcement learning.
A house-cleaning robot that adjusts its path based on route efficiency and dirt removed fits this pattern closely. It acts in an environment, observes results, and improves its behaviour according to an implicit reward such as efficient coverage and cleaning effectiveness. Option B may involve optimization, but the description is less clearly framed around agent-environment interaction and reward-based trial learning.
Option C describes data acquisition for supervised or self-improving image recognition, not reinforcement learning. Option D describes predictive modelling based on historical labelled or structured data, which is more consistent with supervised learning.
References/topics: CT-AI Syllabus Chapter 3, Section 3.1.3 "Reinforcement Learning."
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NEW QUESTION # 38
A neural network has been designed and created to assist day-traders improve efficiency when buying and selling commodities in a rapidly changing market. Suppose the test team executes a test on the neural network where each neuron is examined. For this network the shortest path indicates a buy, and it will only occur when the one-day predicted value of the commodity is greater than the spot price by 0.75%. The neurons are stimulated by entering commodity prices and testers verify that they activate only when the future value exceeds the spot price by at least 0.75%.
Which of the following statements BEST explains the type of coverage being tested on the neural network?
Answer: A
Explanation:
Threshold coverageis a specific type of coverage measure used in neural network testing. It ensures that each neuron in the network achieves an activation value greater than a specified threshold. This is particularly relevant to the scenario described, where testers verify that neurons activate only when the future value of the commodity exceeds the spot price by at least0.75%.
* Threshold-based activation:The test case in the question isexplicitly verifying whether neurons activate only when a certain threshold (0.75%) is exceeded.This aligns perfectly with the definition ofthreshold coverage.
* Common in Neural Network Testing:Threshold coverage is used to measurewhether each neuron in a neural network reaches a specified activation value, ensuring that the neural network behaves as expected when exposed to different test inputs.
* Precedent in Research:TheDeepXplore frameworkused a threshold of0.75%to identify incorrect behaviors in neural networks, making this coverage criterion well-documented in AI testing research.
* (B) Neuron Coverage#
* Neuron coverageonly checks whether a neuron activates (non-zero value)at some point during testing. It does not consider specific activation thresholds, making it less precise for this scenario.
* (C) Sign-Change Coverage#
* This coverage measures whether each neuron exhibitsboth positive and negative activation values, which isnot relevant to the given scenario(where activation only matters when exceeding a specific threshold).
* (D) Value-Change Coverage#
* This coverage requires each neuron to producetwo activation values that differ by a chosen threshold, but the question focuses onwhether activation occurs beyond a fixed threshold, not changes in activation values.
* Threshold coverage ensures that neurons exceed a given activation threshold"Full threshold coverage requires that each neuron in the neural network achieves an activation value greater than a specified threshold. The researchers who created the DeepXplore framework suggested neuron coverage should be measured based on an activation value exceeding a threshold, changing based on the situation." Why is Threshold Coverage Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, asthreshold coverage ensures the neural network's activation is correctly evaluated based on the required condition (0.75%).
NEW QUESTION # 39
Which ONE of the following would be the LEAST effective input to an AI-based test optimization process?
Answer: D
Explanation:
Test environment downtime would be the least effective input to an AI-based test optimization process. While downtime may affect the ability to run tests, it does not provide direct insight into the quality of the tests or the effectiveness of the AI model being tested. In contrast, previously failing tests, defect reports, and source control data are much more relevant as they provide information about areas where the system may have issues or require further optimization.
NEW QUESTION # 40
Which of the below TWO examples of AI system behaviour are reward hacking?
i. An AI medical device intended to keep a patient stable may give the patient a treatment that means they recover less quickly.
ii. An AI system intended to maximise production of a commodity by weight allows quality and size of product to reduce.
iii. An AI system intended to ensure the output of a factory process is always sorted correctly, destroys the outputs.
iv. An AI system intended to remove security vulnerabilities from software code, removes all functionality that has security vulnerabilities.
Answer: A
Explanation:
The correct answer is C , covering examples iii and iv . The CT-AI syllabus defines reward hacking as the situation where an AI-based system achieves a specified goal through a "clever" or "easy" solution that perverts the designer's intent. The goal is technically satisfied, but in a way that defeats the real purpose of the system. The syllabus gives the example of an AI system targeting a highest score by hacking the stored score rather than playing the game properly.
Example iii is reward hacking because destroying the outputs may make the remaining sorting condition trivially true, but it violates the production objective. Example iv is also reward hacking because removing all vulnerable functionality eliminates vulnerabilities by destroying useful system behaviour. Examples i and ii are better categorized as negative side effects : the system pursues a stated goal while ignoring broader quality or stakeholder consequences, such as recovery speed or product quality.
References/topics: CT-AI Syllabus Chapter 2, Section 2.6 "Side Effects and Reward Hacking."
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NEW QUESTION # 41
Which ONE of the following options is a technology used to implement AI?
Answer: C
Explanation:
The correct answer is A. Genetic algorithms . In the CT-AI syllabus, Chapter 1, Section 1.4 "AI Technologies" lists multiple technologies used to implement AI, including fuzzy logic, search algorithms, reasoning techniques, neural networks, Bayesian models, decision trees, random forest, linear regression, logistic regression, clustering algorithms, genetic algorithms , and support vector machines. The syllabus also states that AI-based systems typically implement one or more of these technologies.
Option B, autonomy , is not an AI implementation technology; it is a quality characteristic describing the ability of a system to work without human oversight or intervention for sustained periods. Option C, classification , is a supervised learning problem type in which inputs are assigned to predefined classes, not itself an implementation technology in the syllabus taxonomy. Option D, reinforcement learning , is a form of machine learning, but the option that directly appears in the AI technology list is genetic algorithms .
References/topics: CT-AI Syllabus Chapter 1, Section 1.4 "AI Technologies"; Chapter 3, Section 3.1
"Forms of ML."
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NEW QUESTION # 42
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