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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 4	systems from those required for conventional systems.
Topic 5	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 6	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 7	Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
Topic 8	Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q132-Q137):

NEW QUESTION # 132
Which ONE of the following statements is true about dynamic testing for inappropriate bias?

A. Testing should never be conducted in production
B. Inappropriate bias only needs to be tested when protected characteristics such as race and gender are present in the inputs
C. It can be necessary to obtain additional attributes about the data being processed
D. Reviewing the source of the training data can reveal inappropriate bias

Answer: C

NEW QUESTION # 133
Consider an AI system in which the complex internal structure has been generated by another software system. Why would the tester choose to do black-box testing on this particular system?

A. Black-box testing eliminates the need for the tester to understand the internal structure of the AI system.
B. The tester wishes to better understand the logic of the software used to create the internal structure.
C. The black-box testing method will allow the tester to check the transparency of the algorithm used to create the internal structure.
D. Test automation can be built quickly and easily from the test cases developed during black-box testing.

Answer: A

Explanation:
The syllabus explains:
"Where the internal structure of an AI-based system is too complex for humans to understand, the system can only be tested as a black box. Even when the internal structure is visible, this provides no additional useful information to help with testing." This confirms that black-box testing is chosen because the tester does not need to understand the system's internal structure.

NEW QUESTION # 134
Which ONE of the following statements is MOST true in relation to A/B testing?

A. The purpose is to compare two variants of a system
B. Production data is not used
C. The purpose is to detect defects
D. Many test cases can be generated

Answer: A

Explanation:
A/B testing is primarily used to compare two variants of a system to determine which one performs better. This method involves testing two different versions (A and B) to analyze their impact on specific metrics, often in a real-world environment. The other options do not fully align with the purpose of A/B testing.

NEW QUESTION # 135
Which ONE of the below is MOST likely to be a challenge in ML data preparation?

A. The time and associated costs requited
B. Designing a reward function
C. Functional performance
D. Specification of acceptance criteria

Answer: A

Explanation:
The correct answer is A . The CT-AI syllabus identifies data preparation as one of the most resource-intensive parts of the ML workflow, stating that it uses an average of 43% of ML workflow effort. It also explains that data preparation forms part of the data pipeline and includes acquisition, pre-processing, feature engineering, and related exploratory analysis.
The syllabus lists several data preparation challenges, including the need for domain and data knowledge, difficulty obtaining high-quality data from multiple sources, difficulty automating a scalable and efficient production data pipeline, costs associated with data preparation, defects introduced into the data pipeline, and sample bias.
Option B, functional performance, is a result or evaluation concern for the ML model rather than a data preparation challenge itself. Option C, designing a reward function, is specifically a reinforcement learning challenge. Option D, specifying acceptance criteria, belongs primarily to understanding objectives and defining evaluation targets, not the data preparation activity.
References/topics: CT-AI Syllabus Chapter 4, Section 4.1 "Data Preparation as Part of the ML Workflow"; Section 4.1.1 "Challenges in Data Preparation."
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NEW QUESTION # 136
Which ONE of the following would be the LEAST effective input to an AI based test optimization process?

A. Defect reports
B. Previously failing tests
C. Source control data
D. Test environment downtime

Answer: D

NEW QUESTION # 137
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