CT-AI Certified Tester AI Testing Exam Learning Material in 3 Different Formats

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ISTQB CT-AI Exam Overview:

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

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Exam CT-AI Preview & CT-AI Pdf Version

The CT-AI Exam software’s user-friendly interface is made to uproot potential problems. Once you will try the demo of CT-AI exam questions, you will be well- acquainted with the software and its related features. Also CT-AI exam comes with various self-assessment features like timed exam, randomization questions, and multiple questions types, test history and score etc. Which means it enables you to customize the question type and you may practice random questions in order to enhance your skills and expertise. You may keep attempting the same questions many a time also.

ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 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.
Topic 2	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 3	systems from those required for conventional systems.
Topic 4	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 5	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 6	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 7	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 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	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 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.
Topic 11	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.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q131-Q136):

NEW QUESTION # 131
Which ONE of the following hardware is MOST suitable for implementing Al when using ML?
SELECT ONE OPTION

A. 64-bit CPUs.
B. Hardware supporting high precision floating point operations.
C. High powered CPUs.
D. Hardware supporting fast matrix multiplication.

Answer: D

Explanation:
A . 64-bit CPUs.
While 64-bit CPUs are essential for handling large amounts of memory and performing complex computations, they are not specifically optimized for the types of operations commonly used in machine learning.
B . Hardware supporting fast matrix multiplication.
Matrix multiplication is a fundamental operation in many machine learning algorithms, especially in neural networks and deep learning. Hardware optimized for fast matrix multiplication, such as GPUs (Graphics Processing Units), is most suitable for implementing AI and ML because it can handle the parallel processing required for these operations efficiently.
C . High powered CPUs.
High powered CPUs are beneficial for general-purpose computing tasks and some aspects of ML, but they are not as efficient as specialized hardware like GPUs for matrix multiplication and other ML-specific tasks.
D . Hardware supporting high precision floating point operations.
High precision floating point operations are important for scientific computing and some specific AI tasks, but for many ML applications, fast matrix multiplication is more critical than high precision alone.
Therefore, the correct answer is B because hardware supporting fast matrix multiplication, such as GPUs, is most suitable for the parallel processing requirements of machine learning.

NEW QUESTION # 132
Data used for an object detection ML system was found to have been labelled incorrectly in many cases.
Which ONE of the following options is most likely the reason for this problem?
SELECT ONE OPTION

A. Privacy issues
B. Security issues
C. Bias issues
D. Accuracy issues

Answer: D

Explanation:
The question refers to a problem where data used for an object detection ML system was labelled incorrectly.
This issue is most closely related to "accuracy issues." Here's a detailed explanation:
* Accuracy Issues: The primary goal of labeling data in machine learning is to ensure that the model can accurately learn and make predictions based on the given labels. Incorrectly labeled data directly impacts the model's accuracy, leading to poor performance because the model learns incorrect patterns.
* Why Not Other Options:
* Security Issues: This pertains to data breaches or unauthorized access, which is not relevant to the problem of incorrect data labeling.
* Privacy Issues: This concerns the protection of personal data and is not related to the accuracy of data labeling.
* Bias Issues: While bias in data can affect model performance, it specifically refers to systematic errors or prejudices in the data rather than outright incorrect labeling.
References:This explanation is consistent with the concepts covered in the ISTQB CT-AI syllabus under dataset quality issues and their impact on machine learning models.

NEW QUESTION # 133
Which statement describes factors related to test data that make testing AI-based systems difficult?
Choose ONE option (1 out of 4)

A. Using the same implementation for data acquisition by data scientists and testers prevents defect masking
B. Artificially generated data requires legal approval and must be sanitized and encrypted
C. Creating and managing large amounts of test data can be difficult, especially when it needs to be representative
D. The input data must always be the same over time, especially in real-world systems

Answer: C

Explanation:
Section2.2 - Data Preparationand4.1 - Challenges in Testing AI-Based Systemsdescribe difficulties in obtaining and managing large, representative datasets. AI-based systems requirerealistic, diverse, and representativedata reflecting real-world variations. The syllabus emphasizes that assembling such datasets is time-consuming, resource-intensive, and often constrained by availability, privacy, or domain complexity.
Option B directly corresponds to these documented challenges.
Option A is incorrect: using the same implementation risksdefect masking, not preventing it; the syllabus warns against this practice. Option C is incorrect because real-world data naturally evolves, and the syllabus notes thatdriftis normal; expecting stable input data contradicts operational reality. Option D is incorrect:
although data privacy is important, the syllabus does not claim that artificially generated data always requires legal approval, nor that sanitization/encryption is mandatory for synthetic data.
Thus,Option Baccurately reflects syllabus-defined difficulties in producing representative test data.

NEW QUESTION # 134
"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real- world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
SELECT ONE OPTION
The difficulty of defining criteria for improvement before the model can be accepted.
The fast pace of change did not allow sufficient time for testing.
The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
There was an algorithmic bias in the Al system.

A. The difficulty of defining criteria for improvement before the model can be accepted.
Defining criteria for improvement is a challenge in the acceptance of AI models, but it is not directly related to the performance drop in real-world scenarios. It relates more to the evaluation and deployment phase rather than affecting the model's real-time performance post-deployment.
B. There was an algorithmic bias in the AI system.Algorithmic bias can significantly impact the performance of AI systems. If the model has biases, it will not perform well across different scenarios and data distributions.
C. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
This is highly likely to affect performance. Self-learning AI systems require detailed specifications of the operating environment to adapt and learn effectively. If the environment is insufficiently specified, the model may fail to perform accurately in real-world scenarios.
D. The fast pace of change did not allow sufficient time for testing.
This can significantly affect the model's performance. If the system is self-learning, it needs to adapt quickly, and insufficient testing time can lead to incomplete learning and poor performance.

Answer: A

Explanation:
Given the context of the self-learning nature and the need for real-time adaptability, option A is least likely to describe the fall in performance because it deals with acceptance criteria rather than real-time performance issues.

NEW QUESTION # 135
Consider an AI-system in which the complex internal structure has been generated by another software system. Why would the tester choose to do black-box testing on this particular system?

A. The black-box testing method will allow the tester to check the transparency of the algorithm used to create the internal structure
B. The tester wishes to better understand the logic of the software used to create the internal structure
C. Test automation can be built quickly and easily from the test cases developed during black-box testing
D. Black-box testing eliminates the need for the tester to understand the internal structure of the AI-system

Answer: D

Explanation:
The syllabus explains:
"Where the internal structure of an AI-based system is too complex for humans to understand, the system can only be tested as a black box. Even when the internal structure is visible, this provides no additional useful information to help with testing." This confirms that black-box testing is chosen because the tester does not need to understand the system's internal structure.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 8.5, page 61 of 99)

NEW QUESTION # 136
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