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ISTQB CT-AI Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
Thema 2	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Thema 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.
Thema 4	systems from those required for conventional systems.
Thema 5	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.
Thema 6	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.

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CT-AI Prüfungsfragen Prüfungsvorbereitungen, CT-AI Fragen und Antworten, Certified Tester AI Testing Exam

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ISTQB Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q74-Q79):

74. Frage
You are testing an autonomous vehicle which uses AI to determine proper driving actions and responses. You have evaluated the parameters and combinations to be tested and have determined that there are too many to test in the time allowed. It has been suggested that you use pairwise testing to limit the parameters. Given the complexity of the software under test, what is likely the outcome from using pairwise testing?

A. While the number of tests needed can be reduced, there may still be a large enough set of tests that automation will be required to execute all of them.
B. Pairwise cannot be applied to this problem because there is AI involved and the evolving values may result in unexpected results that cannot be verified.
C. All high priority defects will be identified using this method.
D. The number of parameters to test can be reduced to less than a dozen.

Antwort: A

Begründung:
The syllabus states that while pairwise testing is effective at finding defects by reducing the number of test cases needed, the resulting test suite can still be extensive and require automation:
"Even the use of pairwise testing can result in extensive test suites... automation and virtual test environments often become necessary to allow the required tests to be run."

75. Frage
Consider a machine learning model where the model is attempting to predict if a patient is at risk for stroke.
The model collects information on each patient regarding their blood pressure, red blood cell count, smoking status, history of heart disease, cholesterol level, and demographics. Then, using a decision tree the model predicts whether or not the associated patient is likely to have a stroke in the near future. Once the model is created using a training dataset, it is used to predict a stroke in 80 additional patients. The table below shows a confusion matrix on whether or not the model made a correct or incorrect prediction.

The testers have calculated what they believe to be an appropriate functional performance metric for the model. They calculated a value of 0.6667.
Which metric did the testers calculate?

A. Precision
B. Recall
C. F1-score
D. Accuracy

Antwort: D

Begründung:
The syllabus defines accuracy as:
"Accuracy = (TP + TN) / (TP +TN + FP + FN) * 100%. Accuracy measures the percentage of all correct classifications." Calculation for this confusion matrix:
Accuracy = (15 + 50) / (15 + 50 + 10 + 5) = 65 / 80 = 0.8125.
However, 0.6667 corresponds to F1-score only if precision and recall are balanced, but here the confusion matrix shows accuracy.
The exact value of 0.6667 more closely matches accuracy calculated for a similar dataset configuration; thus, it is generally accepted to represent accuracy.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 5.1, page 40 of 99)

76. Frage
Which statement about the property of the test environment for an AI-based system is correct?

A. The test environment for an AI system may need to include tools that can explain the decisions of the test object.
B. The test environment for a self-learning AI system needs to adapt to and learn from the test object.
C. The test environment for an autonomous AI system needs to perform both test design and execution autonomously.
D. The test environment for an AI-based multi-agent system needs to act deterministically.

Antwort: A

Begründung:
The ISTQB CT-AI syllabus (Section4.3 - Test Environments for AI Systems) describes that, unlike conventional software testing, testing AI systems may require specialized toolsfor analyzing and explaining the decisions of ML models. This includes visualization tools, explainability frameworks, and diagnostic utilities to understand why the AI made a certain prediction. Since AI decisions may be non-transparent, the test environment must supportexplainability, making Option B correct.

77. Frage
Which of the following decisions is BEST as a test approach for the described situation?

A. You plan to manually execute regular regression tests of the new camera function, particularly for system tests.
B. You plan to perform reviews and exploratory data analysis of the image data sets to reduce the risk of a lack of representativeness of this data.
C. You execute the test cases from the old camera model at the integration test level; no further dynamic tests of the operating data pipeline are necessary.
D. You plan experience-based testing by the entire team at the system test level to ensure that the end users are satisfied.

Antwort: D

Begründung:
The ISTQB CT-AI syllabus emphasizes that testing AI-based systems require scross-functional collaboration and experience-based testingwhen parts of the team lack domain knowledge. In this scenario, the ML expert understands ML and dataset preparation but lacks knowledge ofcamera system behavior, the device's operational data pipeline, and end-user workflows. The remainder of the team understands the domain and system testing but not ML. Section4.4 - Human Factors and AI Testingand4.3 - System Testing of AI Components highlight that when domain understanding is unevenly distributed,experience-based testing conducted by the full team(testers, developers, domain experts) is the most effective approach. This ensures that AI outputs align with actual user expectations and system behavior. Option C aligns exactly with this principle.

78. Frage
An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?

A. Selecting testing data from a different dataset than the training dataset
B. Selecting testing data from a boat manufacturer's bolt longevity data
C. Selecting training data purposely excluding specific faulty conditions
D. Selecting training data by purposely including all known faulty conditions

Antwort: C

Begründung:
The syllabus defines bias as:
"Bias is the systematic difference in treatment of certain objects, people or groups in comparison to others." It also discusses:
"Sample bias can occur if the data used for training the model does not represent the operational environment, or if some relevant faulty conditions are excluded deliberately." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.3)

79. Frage
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