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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
Topic 2	ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
Topic 3	Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
Topic 4	Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.
Topic 5	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 6	Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
Topic 7	ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
Topic 8	Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
Topic 9	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

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ISTQB CT-AI Flexible Learning Mode | CT-AI Examcollection Free Dumps

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q74-Q79):

NEW QUESTION # 74
Which data-labeling approach uses a two-step process where labeling is first done by a tool and then verified or completed by a human?
Choose ONE option (1 out of 4)

A. Outsourced data labeling
B. AI-assisted data labeling
C. Crowdsourced data labeling
D. Internal data labeling

Answer: B

Explanation:
Section2.4 - Data Labeling Approachesof the ISTQB CT-AI syllabus explicitly definesAI-assisted data labelingas a hybrid process in which an automated tool performs the initial labeling and human annotators subsequently verify, correct, or complete the labels. This two-step process improves efficiency while retaining human oversight to ensure data quality. The syllabus describes this method as an effective compromise when manual labeling alone would be too slow or costly, and when initial automation can identify obvious patterns before a human provides the final authoritative labels .
Option A (internal labeling) refers to labeling conducted by the organization's own staff but does not imply automation. Option B (crowdsourced labeling) leverages a distributed workforce, typically without automation. Option C (outsourced labeling) transfers labeling tasks to external vendors but similarly does not involve an AI-first step. Only OptionDreflects the two-stage automated-then-human workflow described in the syllabus.
Therefore,AI-assisted data labeling(Option D) is unequivocally correct.

NEW QUESTION # 75
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
SELECT ONE OPTION

A. Testing the data pipeline for any sources for algorithmic bias.
B. Check the input test data for potential sample bias.
C. Testing the distribution shift in the training data for inappropriate bias.
D. Test the model during model evaluation for data bias.

Answer: D

Explanation:
Detecting biases in the ML pipeline involves various tests to ensure fairness and accuracy throughout the ML process.
* Testing the distribution shift in the training data for inappropriate bias (A): This involves checking if there is any shift in the data distribution that could lead to bias in the model. It is an important test but not the most direct method for detecting biases.
* Test the model during model evaluation for data bias (B): This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.
* Testing the data pipeline for any sources for algorithmic bias (C): This test is crucial as it helps identify biases that may originate from the data processing and transformation stages within the pipeline. Detecting sources of algorithmic bias ensures that the model does not inherit biases from these processes.
* Check the input test data for potential sample bias (D): While this is an important step, it focuses more on the input data and less on the overall data pipeline.
Hence, the most likely useful test to help detect different kinds of biases in the ML pipeline isB. Test the model during model evaluation for data bias.
References:
* ISTQB CT-AI Syllabus Section 8.3 on Testing for Algorithmic, Sample, and Inappropriate Bias discusses various tests that can be performed to detect biases at different stages of the ML pipeline.
* Sample Exam Questions document, Question #32 highlights the importance of evaluating the model for biases.

NEW QUESTION # 76
Which ONE of the following statements provides the BEST description of the AI effect?

A. AI systems get better over time as they are exposed to more data.
B. As AI systems improve, the likelihood of reaching the technological singularity increases.
C. The systems used to play chess against human experts in the 1990's are no longer considered AI.
D. AI systems increasingly behave like humans

Answer: C

Explanation:
The correct answer is C because it directly describes the AI effect , where systems once classified as artificial intelligence cease to be regarded as AI after the technology becomes familiar, understood, or superseded. The CT-AI syllabus states that the changing perception of what constitutes AI is known as the "AI Effect," and explains that computer chess systems, including Deep Blue's victory over Garry Kasparov, were formerly viewed as AI but are no longer considered by many to be "true" AI because they used a brute-force approach rather than learning from data or self-learning. Appendix B reinforces this by defining the AI effect as the situation where a previously labelled AI system is no longer considered AI as technology advances.
Option A refers to technological singularity , which belongs to the distinction between general AI and super AI, not the AI effect. Option B describes adaptive or machine-learning behaviour, not a shift in social perception. Option D describes human-like behaviour, which is related to AI capability but not to reclassification over time.
References/topics: CT-AI Syllabus Chapter 1, Section 1.1 "Definition of AI and AI Effect"; Appendix B
"AI effect."

NEW QUESTION # 77
Which statement regarding AI for defect prediction is correct?

A. AI-based defect prediction is most effective when based on previous similar constellations.
B. AI-based defect prediction can detect whether defects exist but not where.
C. AI-based defect prediction is most effective when based on source-code metrics such as branches or McCabe complexity.
D. AI-based defect prediction is based on formal principles and requires only a few factors.

Answer: A

Explanation:
Section5.3 - AI Support for Defect Predictionof the ISTQB CT-AI syllabus explains that AI-based defect prediction models rely onhistorical patterns, including past defects, code behavior, and similar system configurations. ML models trained on prior defect data can identifycomponents likely to contain defectswhen new changes resemble previous defect-inducing patterns. This directly supports Option A, which states that defect prediction is most effective when based on previous similar constellations.

NEW QUESTION # 78
Which ONE of the following statements about a system MOST describes an autonomous system?

A. A chatbot that learns the most effective responses to humans (in order to ensure the humans continue to converse) based on prior experiences.
B. A self-driving car that automatically stops the car if the driver is not responding to voice prompts, in order to ensure they are awake.
C. A fraud detection system that alerts operators when it sees a specific fraud risk associated with a transaction, in order to obtain human input to make a final decision.
D. A loan approval system that can continue to process loan applications indefinitely, within a defined amount of aggregate credit, until an operator reduces the amount of aggregate credit available.

Answer: B

Explanation:
An autonomous system is capable of performing tasks independently, without requiring human intervention. The self-driving car in option B is an example of an autonomous system because it automatically takes action (stopping the car) based on its internal decision-making, in response to the driver's behavior.

NEW QUESTION # 79
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