ISTQB CT-AI Übungsmaterialien - CT-AI Exam Fragen

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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester - AI Testing Exam
Exam Number:	CT-AI
Exam Duration:	60 (75 for non-native language)
Exam Format:	1-2 points per question, Multiple-choice questions
Exam Price:	€180 - €250 (varies by region and provider)
Certificate Validity Period:	Valid indefinitely (no expiration)
Real Exam Qty:	40
Passing Score:	65% (29/44 points for v2.0; 31/47 points for v1.0)
Related Certifications:	ISTQB Certified Tester Foundation Level (CTFL) ISTQB Certified Tester Testing with Generative AI (CT-GenAI)
Available Languages:	Portuguese, Chinese, Spanish, French, German, English, Korean, Japanese
Recommended Training:	CT-AI Syllabus v2.0 ISTQB Accredited Training Providers
Exam Registration:	iSQI Exam Registration Pearson VUE ISTQB Official Registration
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online remote proctored / Onsite test center
Pre Condition:	Must hold ISTQB Certified Tester Foundation Level (CTFL) certification
Official Syllabus URL:	https://istqb.org/certifications/certified-tester-ai-testing-ct-ai/

>> ISTQB CT-AI Übungsmaterialien <<

ISTQB CT-AI Exam Fragen & CT-AI Prüfungsunterlagen

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ISTQB CT-AI Prüfungsplan:

Thema	Einzelheiten
Thema 1	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 2	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
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	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 5	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 6	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 7	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 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.

ISTQB Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q129-Q134):

129. Frage
Before deployment of an AI-based system, a developer is expected to demonstrate in a test environment how decisions are made. Which of the following characteristics does decision making fall under?

A. Explainability
B. Self-learning
C. Non-determinism
D. Autonomy

Antwort: A

Begründung:
The syllabus definesexplainabilityas the ability to understand how the AI-based system comes up with a particular result:
"Explainability is considered to be the ease with which users can determine how the AI-based system comes up with a particular result." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.7)

130. Frage
Which ONE of the following statements is MOST true in relation to A/B testing?

A. Many test cases can be generated
B. Production data is not used
C. The purpose is to detect defects
D. The purpose is to compare two variants of a system

Antwort: D

Begründung:
The correct answer is D . The CT-AI syllabus defines A/B testing as a method where the responses of two variants of a program, A and B, are compared using the same inputs to determine which variant is better. It is a statistical testing approach that typically compares results across several runs. The syllabus also states that a major difference between A/B testing and back-to-back testing is that A/B testing compares two variants of the same system, whereas back-to-back testing is used to detect defects.
Option A is incorrect because A/B testing does not generate test cases and gives no guidance on how tests should be designed, although operational inputs may be used. Option B is more characteristic of back-to-back testing, which uses a pseudo-oracle for defect detection. Option C is incorrect because production or operational inputs are often used, and the syllabus gives examples of e-commerce and web companies using A
/B testing in production to compare consumer responses. Therefore, comparison of two system variants is the defining feature.
References/topics: CT-AI Syllabus Chapter 9, Section 9.4 "A/B Testing"; Section 9.7 "Selecting Test Techniques for AI-Based Systems."

131. Frage
Which characteristic of AI-based systems makes it difficult to ensure they are safe (e.g., not harming humans)?
Choose ONE option (1 out of 4)

A. Robustness
B. Interpretability
C. Determinism
D. Complexity

Antwort: D

Begründung:
The ISTQB CT-AI syllabus lists several characteristics that make it difficult to ensure safety in AI-based systems. Section2.8 - Safety and AIexplicitly names the characteristics that complicate safety assurance:
complexity,non-determinism,probabilistic behavior,self-learning,lack of transparency, andlack of robustness.
Among these,complexityis a core challenge because modern AI systems-particularly those using deep learning-have highly non-linear behavior, large numbers of parameters, and intricate interactions that are hard to predict.
Option B (Complexity) directly aligns with the syllabus and is therefore correct.
Option A (Determinism) is the opposite of AI behavior; AI is oftennon-deterministic, and determinism doesnotmake systems unsafe. Option C (Interpretability) does impact trust and explainability, but the syllabus positions it as a transparency challenge, not the primary difficulty in ensuring safety. Option D (Robustness) is a desired quality, not a reason safety is hard; alackof robustness would be a challenge, not robustness itself.
Thus,complexitybest reflects the syllabus' explicit safety-related difficulty.

132. Frage
Consider a natural language processing (NLP) algorithm that attempts to predict the next word that you would like to type in a text message. An update to the algorithm has been created that should increase the accuracy of the predictions based on user typing patterns. The old algorithm was rated for accuracy by the users. Then, after the new update was released, the users rated the updated algorithm. A statistical test was used to compare between the two versions of the algorithm to see whether or not the update should remain in place.
This is an example of what type of testing?

A. Exploratory testing
B. Metamorphic testing
C. A/B testing
D. Pairwise testing

Antwort: C

Begründung:
A/B testing is a statistical testing method that compares two different versions of a system to determine which one performs better. In this scenario, theold NLP algorithmwas rated for accuracy, and after the update, the new algorithmwas also rated by users. A statistical test was performed to compare the two versions, which is the fundamental approach ofA/B testing.
A/B testing is commonly used in:
* User experience testing(e.g., comparing different versions of a website).
* ML model evaluation(e.g., comparing two AI-based classifiers).
* Performance assessment(e.g., determining if a new recommendation algorithm is more effective).
This approach allows for data-driven decisions, ensuring that any changes to the system result in meaningful improvements.
* Section 9.4 - A/B Testingstates that A/B testing is used to compare updates in AI-based systems to determine if the newer version is better.
Reference from ISTQB Certified Tester AI Testing Study Guide:

133. Frage
Which ONE of the below is NOT an input to an activation function in a neural network?

A. The weights assigned to the connections
B. Bias
C. Activation values from the previous layer
D. Activation values from the current layer

Antwort: D

Begründung:
The activation values from the current layer are the output of the activation function and are not used as an input. The input to the activation function consists of the bias, activation values from the previous layer, and the weights assigned to the connections.

134. Frage
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