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ISTQB CT-AI Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
Thema 2	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Thema 3	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.
Thema 4	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Thema 5	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.
Thema 6	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.

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ISTQB Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q38-Q43):

38. Frage
Which of the following options BEST describes an example of reinforcement learning?

A. An image recognition system regularly searches the internet to find new examples to add to its training data.
B. A health warning system uses data from thousands of people to predict who is most likely to suffer from a particular health condition based on their behaviour.
C. A financial system that chooses which stocks to buy regularly optimizes based on new changes in stock price to improve its performance.
D. A house cleaning robot adjusts the path it takes on a daily basis based on the most efficient route and the amount of dirt it is able to remove.

Antwort: D

Begründung:
The correct answer is A . Reinforcement learning is described in the CT-AI syllabus as an approach in which an intelligent agent learns by interacting with its environment iteratively and learning from experience. The agent receives rewards for correct decisions and penalties for incorrect decisions. The syllabus also identifies robotics, autonomous vehicles, and chatbots as application examples for reinforcement learning.
A house-cleaning robot that adjusts its path based on route efficiency and dirt removed fits this pattern closely. It acts in an environment, observes results, and improves its behaviour according to an implicit reward such as efficient coverage and cleaning effectiveness. Option B may involve optimization, but the description is less clearly framed around agent-environment interaction and reward-based trial learning.
Option C describes data acquisition for supervised or self-improving image recognition, not reinforcement learning. Option D describes predictive modelling based on historical labelled or structured data, which is more consistent with supervised learning.
References/topics: CT-AI Syllabus Chapter 3, Section 3.1.3 "Reinforcement Learning."
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39. Frage
A transportation company operates three types of delivery vehicles in its fleet. The vehicles operate at different speeds (slow, medium, and fast). The transportation company is attempting to optimize scheduling and has created an AI-based program to plan routes for its vehicles using records from the medium-speed vehicle traveling to selected destinations. The test team uses this data in metamorphic testing to test the accuracy of the estimated travel times created by the AI route planner with the actual routes and times.
Which of the following describes the next phase of metamorphic testing?

A. The team decomposes each route into the relevant components that affect the travel time, such as traffic density and vehicle power. The team then uses statistical analysis to characterize the influence of each component to calculate the fast and slow vehicle route times.
B. The team uses an AI system to select the most dissimilar routes. With this information, any of the AI routes can be metaphorically transformed into a fast or slow route.
C. The team tests the time required for the fast and slow vehicles to travel the same route as the medium vehicle. Then, by calculating the speed difference, they then predict how much faster or slower the vehicles will travel. That information is then used to verify that the arrival time of the vehicles meets the expected result.
D. The team uses the same AI route planner to create routes that are longer and shorter but follow the same track. Finally, by driving the fast vehicles on the long routes and slow vehicles on the short routes and vice versa, the AI system will have enough information to infer travel times for all vehicles on all routes.

Antwort: C

Begründung:
The syllabus describes metamorphic testing as:
"Testing involves defining metamorphic relations and then applying those relations to check that the transformations result in expected outcomes, even when the expected output of the system is unknown or not well-defined." In this scenario, applying the metamorphic relation (speed differences) and checking the transformed outcome (arrival times) fits the definition of metamorphic testing.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 9.5, page 69 of 99)

40. Frage
Which ONE of the following describes a situation of back-to-back testing the LEAST?

A. Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
B. Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for same data
C. Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.
D. Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.

Antwort: A

Begründung:
Back-to-back testing is a method where the same set of tests are run on multiple implementations of the system to compare their outputs. This type of testing is typically used to ensure consistency and correctness by comparing the outputs of different implementations under identical conditions.
Let's analyze the options given:
A). Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.
This option describes a scenario where two different implementations of the same type of model are being compared using the same dataset. This is a typical back-to-back testing situation.
B). Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for the same data.
This option involves comparing a custom implementation with a standard implementation, which is also a typical back-to-back testing scenario to validate the custom model against a known benchmark.
C). Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
This option involves comparing two different types of models (a neural network and a decision tree). This is not a typical scenario for back-to-back testing because the models are inherently different and would not be expected to produce identical results even on the same data.
D). Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
This option involves comparing the outputs of the same model on slightly different datasets. This could be seen as a form of robustness testing or sensitivity analysis, but not typical back-to-back testing as it doesn't involve comparing multiple implementations.
Based on this analysis, option C is the one that describes a situation of back-to-back testing the least because it compares two fundamentally different models, which is not the intent of back-to- back testing.

41. Frage
You are developing a "flower" ML model... Which of the following describes an objection that you can NEGLECT in your risk assessment?

A. The possible outputs of the "leaf" and "flower" ML models are so different that reuse has few advantages over new development.
B. The classification behavior of the "flower" ML model is more difficult to understand when it is reused compared to when it is developed from scratch.
C. The probability of misclassification of the ML model "flower" is higher when it is reused than when it is developed from scratch.
D. The possible inputs for the `leaf' and `flower' ML models are so different that reuse has few advantages over new development.

Antwort: A

Begründung:
The ISTQB CT-AI syllabus explains that reusing pre-trained models is strongly related to similarity between the original task and the new task. Section1.8 - Pre-trained Models and Transfer Learningstates that reuse is effective when the new task is similar to the original one, such as adapting a cat-classifier to classify dog breeds. The syllabus warns about risks related toinput differences,data preparation inconsistencies, inherited shortcomings, and explain ability issues. These are legitimate objections (matching options A, B, and C) because large differences in image inputs or patterns can undermine transfer learning; misclassification risk can increase; and explainability often decreases when reusing pre-trained models .
However, output differences are NOT a valid concernhere. Both the leaf-based and flower-based ML models classify the same plant species, meaning theiroutputs are identical. The syllabus does not identify output mismatch as a transfer-learning risk. Real risks concerninputs,bias inheritance,model transparency, andtraining differences--not output labels. Therefore, Option D describes an objection that can be safely neglected, because output classes are the same and do not hinder reuse.

42. Frage
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. Internal data labeling
D. Crowdsourced data labeling

Antwort: B

Begründung:
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.

43. Frage
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CT-AI Vorbereitungsfragen: https://www.zertsoft.com/CT-AI-pruefungsfragen.html

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