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2025 Latest Test CT-AI Discount | Efficient ISTQB CT-AI Valid Exam Syllabus: Certified Tester AI Testing Exam

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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

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 3

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 4

systems from those required for conventional systems.

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

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 7

Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q69-Q74):

NEW QUESTION # 69
Which machine learning approach is most suitable for predicting customer purchase probability?
Choose ONE option (1 out of 4)

A. Supervised learning (classification)
B. Unsupervised learning
C. Supervised learning (regression)
D. Reinforcement learning

Answer: A

Explanation:
The ISTQB CT-AI syllabus explains in Section1.6 - Machine Learning Approachesthatsupervised learning is appropriate when labeled data exists and the goal is to predict an output based on known historical examples. Predicting a customer'spurchase probabilityis aclassificationtask when the output corresponds to discrete categories such as"likely to purchase"vs."not likely to purchase."The syllabus gives similar examples in describing classification as the process of assigning instances to predefined classes based on learned patterns in labeled data. Because the retail company wants to determine whether a customer will make a purchase based on marketing actions, classification is the most appropriate choice .
Option A (regression) predicts continuous numeric values and is less suitable because the task centers on categorical likelihood, not estimating exact monetary values. Option C (unsupervised learning) is used when labels are not available-here, the company has labeled purchase histories. Option D (reinforcement learning) requires an interactive environment with reward-driven behavior, which is not applicable to this scenario.
Thus,supervised learning (classification)is the most suitable approach according to the syllabus.

NEW QUESTION # 70
The training of an ML model... What type of bias is LEAST important to look for when testing the model?
Choose ONE option (1 out of 4)

A. Inappropriate bias
B. Sample bias
C. Automation bias
D. Algorithmic bias

Answer: C

Explanation:
The ISTQB CT-AI syllabus distinguishes between several types of bias relevant in AI testing, including sample bias,algorithmic bias, andinappropriate bias. In Section3.3 - Bias in AI-Based Systems, the syllabus stresses the importance of identifying biases that originate fromtraining data,model development, anddecision logic. Sample bias occurs when the training data does not adequately represent the population; algorithmic bias arises when the model produces systematically skewed results due to learned patterns; inappropriate bias involves ethically or socially problematic distortions in the outcomes. All three of these bias types directly affect theoutputs of the AI modeland are therefore highly relevant when testing an industrial inspection system intended to reliably detect defects. These biases can lead to defective items being missed or false alarms being raised, which impacts quality assurance significantly .
Automation bias, however, is fundamentally different. It refers to ahuman cognitive bias, where users (e.g., inspectors) overly trust or rely on the AI system's output. While important in user-interaction testing, it isnota biaswithin the ML model itself. Since the question asks which bias isleast important when testing the model, automation bias can be legitimately deprioritized duringmodel-level testing. Therefore, OptionBis correct.

NEW QUESTION # 71
Which AI-specific test objective and acceptance criterion should be selected MOST LIKELY for testing GPT_Legal?
Choose ONE option (1 out of 4)

A. Test objective: Evidence of functional safety
Acceptance criterion: The system recognizes failures in the transmission of information and data with the DPMA system and the evaluation system by means of self-tests.
B. Test objective: Evidence that the data is free from inappropriate bias Acceptance criterion: The DPMA's analysis data is statistically compared to data from other sources.
C. Test objective: Evidence of compatibility
Acceptance criterion: The system can exchange information with the DPMA system and the evaluation system.
D. Test objective: Evidence of evolution
Acceptance criterion: The quality of the research results does not deteriorate with further training.

Answer: D

Explanation:
The ISTQB CT-AI syllabus introducesAI-specific quality characteristics, includingevolution,functional safety,compatibility, andbias-related data quality. Section5.1 - AI-Specific Test Objectivesexplains that evolutionrefers to an AI system's capability to continue improving or at least maintain performance as it undergoes additional training. GPT_Legal is explicitly described as aself-learning systemexpected to:
* continuously reduce false positives,
* achieve weekly accuracy improvements of 10%,
* reach and maintain 90% accuracy,
* adapt to new environments (patent law firm # corporate legal department).
This aligns perfectly with the syllabus definition ofevidence of evolution: ensuring the model doesnot degrade as additional training data is introduced. OptionBtherefore directly supports the described acceptance criteria for this evolving, self-learning application.
Option A (functional safety) is irrelevant because patent searching and drafting do not constitute safety- critical domains. Option C (compatibility) is necessary but not the primary AI-specific objective. Option D addresses bias, which is important but not central to the described performance and continuous-learning expectations.
Thus,Option Bis the most appropriate AI-specific test objective.

NEW QUESTION # 72
Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?
SELECT ONE OPTION

A. Genetic algorithms
B. Search engines
C. Procedural programming
D. Case control structures

Answer: A

Explanation:
* Technology Most Typically Used to Implement AI: Genetic algorithms are a well-known technique used in AI . They are inspired by the process of natural selection and are used to find approximate solutions to optimization and search problems. Unlike search engines, procedural programming, or case control structures, genetic algorithms are specifically designed for evolving solutions and are commonly employed in AI implementations.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 1.4 AI Technologies, which identifies different technologies used to implement AI.

NEW QUESTION # 73
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer).
A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I.Pairwise testing of combinations
II.Testing each individual model for accuracy
III.A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION

A. I and III
B. Only II
C. I and II
D. Only III

Answer: C

Explanation:
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
* Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
* Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.
* A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
:
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.

NEW QUESTION # 74
......

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