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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester AI Testing (CT-AI) Exam
Exam Number:	CT-AI
Exam Duration:	60 minutes
Exam Format:	Multiple Choice
Passing Score:	65%
Related Certifications:	ISTQB Certified Tester Foundation Level (CTFL)
Certificate Validity Period:	Lifetime
Available Languages:	English
Real Exam Qty:	40
Recommended Training:	ISTQB Accredited Training Providers
Exam Registration:	ISTQB Official Website
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online and onsite proctored exam via accredited ISTQB examination providers
Pre Condition:	Recommended prior knowledge of ISTQB Foundation Level (CTFL) and basic understanding of software testing concepts and machine learning fundamentals.
Official Syllabus URL:	https://www.istqb.org

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 3	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 4	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 5	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 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.
Topic 7	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.
Topic 8	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.
Topic 9	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.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
Which ONE of the following statements about a system MOST describe an autonomous system?

A. A self driving car that automatically stops the car if the driver is not responding to voice prompts, in order to ensure they are awake.
B. A chatbot that learns the most effective responses to humans (in order to ensure the humans continue to converse) based on prior experiences.
C. A loan approval system that can continue to process loan applications indefinitely, within a defined amount of aggregate credit, until an operator reduces the amount of aggregate credit available.
D. A fraud detection system that alerts operators when it sees a specific fraud risk associated with a transaction, in order to obtain human input to make a final decision.

Answer: C

Explanation:
The correct answer is A because it best matches the CT-AI concept of autonomy: the system can operate independently for a prolonged period, processing loan applications within predefined constraints until a human changes the available aggregate credit. The syllabus defines autonomy as the ability of a system to work independently of human oversight and control for prolonged periods, and emphasizes that testers should identify how long the system is expected to perform satisfactorily without human intervention and when control should be returned to humans.
Option B describes a safety or driver-monitoring function rather than sustained independent operation; the system reacts when the driver is unresponsive but still assumes human supervision. Option C describes evolution or self-learning, because the chatbot improves responses based on prior interactions, but self- learning alone does not make the system autonomous. Option D is explicitly human-in-the-loop: the fraud system only alerts operators and requires human input for the final decision, so it is less autonomous.
References/topics: CT-AI Syllabus Chapter 2, Section 2.2 "Autonomy"; Appendix B "Autonomous system" and "Autonomy."

NEW QUESTION # 20
A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?

A. Clustering
B. Classification
C. Reinforcement learning
D. Regression

Answer: B

Explanation:
Classification: This type of machine learning involves categorizing input data into predefined classes. In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).

NEW QUESTION # 21
A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?
SELECT ONE OPTION

A. Clustering
B. Classification
C. Reinforcement learning
D. Regression

Answer: B

Explanation:
Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification. Here's a breakdown:
Classification: This type of machine learning involves categorizing input data into predefined classes. In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).
Why Not Other Options:
Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.
Regression: This is used for predicting continuous values, not discrete categories like digit recognition.
Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.

NEW QUESTION # 22
Which ONE of the following statements about the hardware used to implement ML systems is MOST likely to be correct?

A. Support for complex operations is required
B. A higher clock speed is required
C. Specialist hardware is necessary for ML
D. Less bits are required for hardware supporting ML

Answer: A

Explanation:
ML systems often require hardware that supports complex operations, such as matrix multiplications and other linear algebra computations. These operations are fundamental to many machine learning algorithms, particularly in deep learning. Specialist hardware (e.g., GPUs) may be used for efficiency, but complex operations are the core requirement.

NEW QUESTION # 23
Which ONE of the following activities is MOST relevant when addressing the scenario where you have more than the required amount of data available for the training?
SELECT ONE OPTION

A. Data augmentation
B. Data sampling
C. Feature selection
D. Data labeling

Answer: B

Explanation:
A . Feature selection
Feature selection is the process of selecting the most relevant features from the data. While important, it is not directly about handling excess data.
B . Data sampling
Data sampling involves selecting a representative subset of the data for training. When there is more data than needed, sampling can be used to create a manageable dataset that maintains the statistical properties of the full dataset.
C . Data labeling
Data labeling involves annotating data for supervised learning. It is necessary for training models but does not address the issue of having excess data.
D . Data augmentation
Data augmentation is used to increase the size of the training dataset by creating modified versions of existing data. It is useful when there is insufficient data, not when there is excess data.
Therefore, the correct answer is B because data sampling is the most relevant activity when dealing with an excess amount of data for training.

NEW QUESTION # 24
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