Specifications of ISTQB CT-AI Practice Exam Software

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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester - AI Testing Exam
Exam Number:	CT-AI
Real Exam Qty:	40
Exam Price:	€180 - €250 (varies by region and provider)
Exam Format:	Multiple-choice questions, 1-2 points per question
Related Certifications:	ISTQB Certified Tester Testing with Generative AI (CT-GenAI) ISTQB Certified Tester Foundation Level (CTFL)
Certificate Validity Period:	Valid indefinitely (no expiration)
Available Languages:	English, Portuguese, French, Korean, Chinese, Spanish, Japanese, German
Passing Score:	65% (29/44 points for v2.0; 31/47 points for v1.0)
Exam Duration:	60 (75 for non-native language)
Recommended Training:	ISTQB Accredited Training Providers CT-AI Syllabus v2.0
Exam Registration:	iSQI Exam Registration ISTQB Official Registration Pearson VUE
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/

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CT-AI Valid Dumps Pdf & CT-AI Dump Check

The learning material is available in three different easy-to-use forms. The first one is a PDF form. The students can save the CT-AI questions by taking out their prints or can access them on their smartphones, tablets, and laptops. The PDF form can be used anywhere anytime and is essential for applicants who like to learn from their smart devices. The second form is Certified Tester AI Testing Exam (CT-AI) web-based practice test which can be taken from browsers.

ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 2	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.
Topic 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.
Topic 4	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 5	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.
Topic 6	systems from those required for conventional systems.
Topic 7	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.
Topic 8	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.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q10-Q15):

NEW QUESTION # 10
A robotic AI-based system is being built by a logistics company to operate within its unmanned warehouses. The warehouses can all be very different and new ones are being added each year.
It is expected that the system will not require retraining for each warehouse, and will be able to learn the location of different items and move them to specified locations on request.
Which ONE of the following attributes should be MOST carefully considered when specifying the objectives and acceptance criteria for the system?

A. Adaptability
B. Inappropriate bias
C. Ethics
D. Explainability

Answer: A

Explanation:
Adaptability is the most important attribute to consider in this scenario. Since the system will operate in different warehouses, each with potentially different layouts and configurations, it must be adaptable to various environments without needing retraining. The system should be able to learn and adjust to the specific characteristics of each warehouse, ensuring it functions effectively in all locations.

NEW QUESTION # 11
Which ONE of the below is NOT an input to an activation function in a neural network?

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

Answer: B

Explanation:
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.

NEW QUESTION # 12
Which statement regarding flexibility and adaptability of AI-based systems is correct?
Choose ONE option (1 out of 4)

A. Self-learning AI-based systems are classified according to whether they are adaptable only or flexible only.
B. Adaptability and flexibility are important when the system needs to change its behavior and determine the change on its own.
C. Flexibility is considered to be the ease with which the system can be reprogrammed to a changed operating condition.
D. Adaptability is considered to be the ability of the system to be used in unspecified situations.

Answer: B

Explanation:
The ISTQB CT-AI syllabus defines these two concepts clearly inSection 2.1 - Flexibility and Adaptability.
Flexibility is described as the ability of a system to operate in situationsnot explicitly covered in its original requirements, while adaptability refers to how easily the system can bemodifiedto handle new environments or conditions. The syllabus stresses that both flexibility and adaptability are crucial, particularly inself- learning AI systemsthat may need to respond to changes in their environment and adjust their behavior accordingly. It states that systems must be capable of determiningwhenandhowto adjust behavior in evolving situations, especially when the operational environment is not fully known at deployment time . This directly aligns with OptionA.
Option B reverses definitions-the syllabus states flexibility (not adaptability) relates to unspecified situations. Option C is incorrect: self-learning systems requirebothflexibility and adaptability; they are not categorized as one or the other. Option D incorrectly defines flexibility; the syllabus defines adaptability-not flexibility-as ease of modification.
Thus,Option Acorrectly reflects the syllabus.

NEW QUESTION # 13
Which of the following statements regarding experience-based testing for AI-based systems is correct?

A. Tour refers to intuitive test case design for AI-based systems based on multiple, sequential test cases using systematically biased training data.
B. In checklist-based testing of AI-based systems, the existing test cases are dynamically adapted, for example based on metamorphic testing.
C. Exploratory testing is often used for AI-based systems because there are often insufficient specifications or problems with the test oracle for AI-based systems.
D. Intuitive test case design for AI-based systems involves interactive, hypothesis-driven examination of data for correlations or developmental trends.

Answer: C

Explanation:
The ISTQB CT-AI syllabus explains inSection 4.4 - Experience-Based Testing for AI Systemsthat AI-based systems frequently suffer frominsufficient specifications, unpredictable model behavior, andtest oracle problems, especially when outputs depend on probabilistic or learned patterns.
The syllabus explicitly states thatexploratory testingis especially valuable in such contexts because it allows testers to investigate the system interactively, observe unexpected behavior, and evaluate system responses that cannot be fully predicted beforehand. Thus, Option C accurately reflects the role and justification of exploratory testing for AI systems.

NEW QUESTION # 14
Which of the following statements about explainable AI is correct?
Choose ONE option (1 out of 4)

A. According to The Royal Society, one reason for explainable AI is to increase user confidence in the system
B. Explainability refers to how easily the algorithms and training data needed to create the model can be determined
C. According to The Royal Society, one reason for explainable AI is to eliminate the need for risk and vulnerability assessments
D. Interpretability refers to how easily users can determine whether the result provided by the AI-based system is correct

Answer: A

Explanation:
Section2.10 - Explainability and Transparencyof the ISTQB CT-AI syllabus describes explainable AI as the ability of a system to provide human-understandable insight into its decisions. The syllabus referencesThe Royal Society's reportas a foundational source explaining why explainability is important. Among the stated motivations is the need toincrease user trust and confidencein AI systems by making their decisions understandable and justifiable. Therefore, OptionCdirectly reflects the syllabus content .
Option A is incorrect because interpretability doesnotrefer to determining correctness of outputs; rather, it refers to understandinghowthe model arrives at outputs. Option B incorrectly frames explainability as the ability to investigate algorithms or training data; explainability is aboutunderstanding the model's decision- making, not reverse engineering its components. Option D is incorrect because explainability doesnot eliminate the need for risk and vulnerability assessments; the syllabus clearly emphasizes that testing, risk assessment, and robustness checks remain critical even when a model is explainable.
Thus, the only statement consistent with the syllabus isOption C.

NEW QUESTION # 15
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