CT-AI Testing Engine, CT-AI Probesfragen

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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	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.
Thema 3	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.
Thema 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.
Thema 5	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Thema 6	Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
Thema 7	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Thema 8	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 9	systems from those required for conventional systems.
Thema 10	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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CT-AI Probesfragen, CT-AI Exam

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ISTQB Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q148-Q153):

148. Frage
In a certain coffee producing region of Colombia, there have been some severe weather storms, resulting in massive losses in production. This caused a massive drop in stock price of coffee.
Which ONE of the following types of testing SHOULD be performed for a machine learning model for stock-price prediction to detect influence of such phenomenon as above on price of coffee stock.
SELECT ONE OPTION

A. Testing for concept drift
B. Testing for security
C. Testing for bias
D. Testing for accuracy

Antwort: A

Begründung:
* Type of Testing for Stock-Price Prediction Models: Concept drift refers to the change in the statistical properties of the target variable over time. Severe weather storms causing massive losses in coffee production and affecting stock prices would require testing for concept drift to ensure that the model adapts to new patterns in data over time.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 7.6 Testing for Concept Drift, which explains the need to test for concept drift in models that might be affected by changing external factors.

149. Frage
Which of the following is a problem with AI-generated test cases that are generated from the requirements?

A. They are slow and will usually not be able to execute in the time allowed.
B. They make debugging more complicated because the number of steps is usually high in order to induce the target failure.
C. They are usually missing the expected results, so verification is difficult or must resort to only detecting significant failures.
D. They are defect prone because they are unable to detect nuances in the requirements.

Antwort: C

Begründung:
AI-generated test cases are often created using machine learning (ML) models or heuristic algorithms. While these can be effective in generating large numbers of test cases quickly, they oftensuffer from the "test oracle problem."
* Test Oracle Problem:A test oracle is the mechanism used to determine the expected output of a test case. AI-generated test cases oftenlack expected resultsbecause AI-based tools do not inherently understand what the correct output should be.
* Difficulty in Verification:Without expected results, verifying test cases becomes challenging. Testers mustrely on heuristics, anomaly detection, or significant failures, rather than traditional pass/fail conditions.
* A (Slow Execution Time):AI-generated tests are typically automated and designed for efficiency. They are not inherently slow and often executefasterthan manually written tests.
* B (Defect-Prone Due to Nuance Issues):While AI-generated tests may struggle with some complexities in requirements, they primarilylack expected results, rather than failing due to an inability to detect nuances.
* C (Complicated Debugging Due to Many Steps):AI-generated testsreducedebugging complexity by limiting the number of steps required to reproduce failures.
* ISTQB CT-AI Syllabus (Section 11.3: Using AI for Test Case Generation)
* "AI-generated test cases often lack expected results, making it difficult to verify correctness without a test oracle.".
* "Verification often relies on detecting significant failures rather than having predefined expected results.".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since AI-generated test cases frequentlylack expected results, verification becomes difficult, requiring testers tofocus on major failuresrather than precise pass/fail conditions. Thus, thecorrect answer is D.

150. Frage
Which supervised-learning classification/regression statement is correct?

A. Recognizing a dog from many different images is a regression problem
B. Deciding whether an object is a bicycle or a motorcycle is a classification problem
C. Predicting that diesel prices will increase by ~10% is a classification problem
D. In classification, objects are always assigned to exactly two classes

Antwort: B

Begründung:
The ISTQB CT-AI syllabus explains supervised learning under Section1.6 - Machine Learning Approaches. It defines classification as predicting categorical labels, where as regression predicts continuous numerical values. OptionB--deciding whether an object is a bicycle or a motorcycle-- fits the definition of classification precisely because the model chooses between discrete categories. The syllabus also uses similar examples to illustrate classification tasks, reinforcing that this is the correct interpretation.

151. Frage
Which ONE of the below types of testing is NOT a type of experience based testing applied to an AI based system?

A. Using an industry checklist to assess the steps used to prepare an ML system
B. Exploring training data to understand its variety, patterns, structure and shape - then using that to inform testing
C. Using knowledge about previous ML systems to identify potential biases
D. Usability testing of the format of the output predictions

Antwort: D

Begründung:
The correct answer is B . The CT-AI syllabus identifies experience-based testing as including error guessing, exploratory testing, and checklist-based testing , all of which may be applied to AI-based systems. It also explicitly relates exploratory testing to exploratory data analysis, where data is examined for patterns, relationships, trends, outliers, distribution, format, and ranges.
Option A matches exploratory testing and EDA, because the tester explores training data to guide subsequent testing. Option C matches error guessing, because the tester uses knowledge of previous ML-system failures, such as biased training data, to identify likely faults. Option D matches checklist-based testing, because the syllabus cites Google's ML test checklist as an effective checklist approach for ML systems.
Option B is not, by itself, an experience-based AI testing type. Usability testing may be important for AI- based systems, especially where users consume predictions, recommendations, or explanations, but it is classified as a quality-characteristic or user-experience concern, not as one of the named experience-based techniques in this syllabus context.
References/topics: CT-AI Syllabus Chapter 9, Section 9.6 "Experience-Based Testing of AI-Based Systems"; Section 9.6.1 "Exploratory Testing and EDA."
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152. Frage
You are developing a "flower" ML model... Which of the following describes an objection that you can NEGLECT in your risk assessment?
Choose ONE option (1 out of 4)

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

Antwort: B

Begründung:
The ISTQB CT-AI syllabus explains that reusing pre-trained models is strongly related tosimilarity 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, andexplainability 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 classifythe 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, OptionDdescribes an objection that can be safely neglected, because output classes are the same and do not hinder reuse.

153. Frage
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