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NEW QUESTION # 85
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: C
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 # 86
Consider a natural language processing (NLP) algorithm that attempts to predict the next word that you would like to type in a text message. An update to the algorithm has been created that should increase the accuracy of the predictions based on user typing patterns. The old algorithm was rated for accuracy by the users. Then, after the new update was released, the users rated the updated algorithm. A statistical test was used to compare the two versions of the algorithm to see whether or not the update should remain in place.
This is an example of what type of testing?
Answer: D
Explanation:
The syllabus states:
"A/B testing can be used to test updates to an AI-based system where there are agreed acceptance criteria, such as ML functional performance metrics, as described in Chapter 5. A/B testing is used to compare the updated variant with the previous variant." (Reference: ISTQB CT-AI Syllabus v1.0, Section 9.4, page 68 of 99)
NEW QUESTION # 87
Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION
Answer: B
Explanation:
Clustering is a type of machine learning technique used to group similar data points into clusters. It is a key concept in unsupervised learning, where the algorithm tries to find patterns or groupings in data without prior knowledge of output classes. Let's analyze each option:
A . Clustering is classification of a continuous quantity.
This is incorrect. Classification typically involves discrete categories, whereas clustering involves grouping similar data points. Classification of continuous quantities is generally referred to as regression.
B . Clustering is supervised learning.
This is incorrect. Clustering is an unsupervised learning technique because it does not rely on labeled data.
C . Clustering is done without prior knowledge of output classes.
This is correct. In clustering, the algorithm groups data points into clusters without any prior knowledge of the classes. It discovers the inherent structure in the data.
D . Clustering requires you to know the classes.
This is incorrect. Clustering does not require prior knowledge of classes. Instead, it aims to identify and form the classes or groups based on the data itself.
Therefore, the correct answer is C because clustering is an unsupervised learning technique done without prior knowledge of output classes.
NEW QUESTION # 88
Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?
SELECT ONE OPTION
Answer: A
Explanation:
* A. Using a pixel comparison of the GUI before and after the change to check the differences.
Pixel comparison is a traditional method and does not involve AI . It compares images at the pixel level, which can be effective but is not an intelligent approach. It is not considered an AI usage and is the least likely usage of AI for detecting GUI changes.
* B. Using computer vision to compare the GUI before and after the test object changes.
Computer vision involves using AI techniques to interpret and process images. It is a likely usage of AI for detecting changes in the GUI .
* C. Using vision-based detection of the GUI layout changes before and after test object changes.
Vision-based detection is another AI technique where the layout and structure of the GUI are analyzed to detect changes. This is a typical application of AI .
* D. Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.
An ML-based classifier can intelligently determine significant changes and decide if they need human review, which is a sophisticated AI application.
NEW QUESTION # 89
Which ONE of the following would be the MOST effective input to an AI-based defect prediction tool?
Answer: A
Explanation:
Cyclomatic complexity would be the most effective input to an AI-based defect prediction tool.
Cyclomatic complexity is a software metric that measures the complexity of a program's control flow, which is closely related to the likelihood of defects. Higher complexity generally indicates a higher probability of defects. This makes it a strong predictor of potential issues in the code, and thus a valuable input for defect prediction.
NEW QUESTION # 90
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