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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	systems from those required for conventional systems.
Topic 3	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.
Topic 4	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 5	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 6	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 7	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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
Which of the following is a dataset issue that can be resolved using pre-processing?

A. Numbers stored as strings
B. Insufficient data
C. Wanted outliers
D. Invalid data

Answer: A

NEW QUESTION # 13
There is a growing backlog of unresolved defects for your project. You know the developers have an ML model that they have created which has learned which developers work on which type of software and the speed with which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?

A. Use it to determine the root cause of each defect and develop a process improvement plan that can be implemented to remove the most common root causes
B. Use it to prioritize defects automatically based on the time expected for the fix to be made, the speed of the fix, and the likelihood of regressions
C. Use it to review the code and determine where more defects are likely to occur so that testing can be targeted to those areas
D. Use it to assign defects to the best developer to resolve the problem and to load balance the defect assignments among the developers

Answer: D

Explanation:
The syllabus explains that ML models can be used to analyze reported defects and suggest which developers are best suited to fix them based on historical data about defect assignment and resolution speed:
"Assignment: ML models can suggest which developers are best suited to fix particular defects, based on the defect content and previous developer assignments." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.2, page 78 of 99)

NEW QUESTION # 14
Which ONE of the following approaches to labelling requires the least time and effort?

A. Internal
B. Al-Assisted
C. Pre-labeled dataset
D. Outsourced

Answer: C

Explanation:
Labelling Approaches: Among the options provided, pre-labeled datasets require the least time and effort because the data has already been labeled, eliminating the need for further manual or automated labeling efforts.

NEW QUESTION # 15
A company producing consumable goods wants to identify groups of people with similar tastes for the purpose of targeting different products for each group. You have to choose and apply an appropriate ML type for this problem.
Which ONE of the following options represents the BEST possible solution for this above- mentioned task?

A. Association
B. Clustering
C. Regression
D. Classification

Answer: B

Explanation:
Clustering is an unsupervised learning method used to group similar data points based on their features. It is ideal for identifying groups of people with similar tastes without prior knowledge of the group labels. This technique will help the company segment its customer base effectively.

NEW QUESTION # 16
How can a tester check the system for bias as part of a review of data sources, acquisition, and preprocessing?
Choose ONE option (1 out of 4)

A. As part of the review of preprocessing, it can reveal whether the data has been influenced in a way that could lead to algorithmic bias.
B. During the review of the preprocessing, the auditor can uncover whether the data has been influenced in a way that could lead to sample distortions.
C. During the review, it can uncover algorithmic bias by analysing the procedures used to obtain the training data.
D. It may use the LIME method as part of its data collection review to detect inappropriate bias.

Answer: B

Explanation:
Bias detection at thedata levelis performed by reviewingdata acquisition and preprocessing steps, as explained in Section2.3 - Data Quality and Biasof the ISTQB CT-AI syllabus. Sample bias arises when data is distorted or when preprocessing introduces unintended shifts-for example, by filtering, normalization, or labeling steps that disproportionately affect subsets of the data. OptionBcorrectly reflects this: reviewers can identify whether preprocessing steps have altered the dataset in a way that introducessample distortions. This aligns perfectly with syllabus guidance on reviewing data pipelines for bias sources.
Option A is incorrect because algorithmic bias originates from themodel, not data collection procedures.
Option C is incorrect because LIME is anexplainabilitymethod applied post-model, not in data reviews.
Option D incorrectly states "algorithmic bias," but preprocessing affectssample bias, not algorithmic bias.
Thus, OptionBcorrectly matches the syllabus' definition of how bias can be detected during data-related reviews.

NEW QUESTION # 17
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