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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
Real Exam Qty:	40
Available Languages:	English
Passing Score:	65%
Certificate Validity Period:	Lifetime
Exam Format:	Multiple Choice
Exam Duration:	60 minutes
Related Certifications:	ISTQB Certified Tester Foundation Level (CTFL)
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 Prüfungsplan:

Thema	Einzelheiten
Thema 1	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 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.
Thema 3	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 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	systems from those required for conventional systems.
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	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 8	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 9	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 10	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Thema 11	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

ISTQB Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q149-Q154):

149. Frage
Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION

A. Self-learning systems are expected to deal with new situations without explicitly having to program for it.
B. Flexible Al systems allow for easier modification of the system as a whole.
C. Al systems require changing of operational environments; therefore, flexibility is required.
D. Al systems are inherently flexible.

Antwort: B

Begründung:
Flexibility in AI systems is crucial for various reasons, particularly because it allows for easier modification and adaptation of the system as a whole.
* AI systems are inherently flexible (A): This statement is not correct. While some AI systems may be designed to be flexible, they are not inherently flexible by nature. Flexibility depends on the system's design and implementation.
* AI systems require changing operational environments; therefore, flexibility is required (B):
While it's true that AI systems may need to operate in changing environments, this statement does not directly address the importance of flexibility for the modification of the system.
* Flexible AI systems allow for easier modification of the system as a whole (C): This statement correctly describes the importance of flexibility. Being able to modify AI systems easily is critical for their maintenance, adaptation to new requirements, and improvement.
* Self-learning systems are expected to deal with new situations without explicitly having to program for it (D): This statement relates to the adaptability of self-learning systems rather than their overall flexibility for modification.
Hence, the correct answer isC. Flexible AI systems allow for easier modification of the system as a whole.
:
ISTQB CT-AI Syllabus Section 2.1 on Flexibility and Adaptability discusses the importance of flexibility in AI systems and how it enables easier modification and adaptability to new situations.
Sample Exam Questions document, Question #30 highlights the importance of flexibility in AI systems.

150. Frage
Which of the following is an example of an input change where it would be expected that the AI system should be able to adapt?

A. It has been trained to analyze customer buying trend data and is given information on supplier cost data.
B. It has been trained to recognize cats and is given an image of a dog.
C. It has been trained to analyze mathematical models and is given a set of landscape pictures to classify.
D. It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution.

Antwort: D

Begründung:
The syllabus explains that input changes that arein the same domainas what was used for training are expected to be handled with adaptability:
"Adaptability refers to the ability of a system to adjust its behavior in response to changes in its environment or inputs. This includes changes to the inputs which are still within the expected operational range of the system, such as resolution changes in images or sensor data."

151. Frage
Which ONE of the following options for a test basis would give the LEAST coverage when using AI-based test generation?

A. A pseudo-oracle
B. A test model describing the application functionality
C. A list of possible web pages that comprise the application
D. An XML schema

Antwort: D

Begründung:
An XML schema would give the least coverage when using AI-based test generation. While it defines the structure of data (e.g., tags, elements), it does not provide detailed information about the application functionality or behavior, which are essential for generating meaningful tests for AI systems. The other options (test model, web pages list, and pseudo-oracle) provide more comprehensive insights into the system's behavior and logic, which are better suited for effective AI-based test generation.

152. Frage
Which ONE of the following statements about the hardware used to implement ML systems is MOST likely to be correct?

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

Antwort: B

Begründung:
The correct answer is B. Less bits are required for hardware supporting ML . The CT-AI syllabus explains that ML typically benefits from hardware supporting low-precision arithmetic , which uses fewer bits for computation, for example 8 bits instead of 32 bits, and states that this is usually sufficient for ML. It also identifies two other beneficial hardware attributes: the ability to work with large data structures and massively parallel processing.
Option A is too absolute. Specialist hardware may be useful, and AI-specific processors exist, but the syllabus states that a model may run on a low-end smartphone and that cloud training with later deployment to a host device is common. Option C is incorrect because general-purpose CPUs support complex operations that are not typically required for ML applications. Option D is incorrect because higher clock speed alone is not the deciding factor; the syllabus notes that GPUs may outperform CPUs for ML despite CPUs often having faster clock speeds.
References/topics: CT-AI Syllabus Chapter 1, Section 1.6 "Hardware for AI-Based Systems."
=========

153. Frage
Which ONE of the following describes a situation of back-to-back testing the LEAST?
SELECT ONE OPTION

A. Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
B. Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
C. Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for same data
D. Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.

Antwort: B

Begründung:
Back-to-back testing is a method where the same set of tests are run on multiple implementations of the system to compare their outputs. This type of testing is typically used to ensure consistency and correctness by comparing the outputs of different implementations under identical conditions. Let's analyze the options given:
A . Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.
This option describes a scenario where two different implementations of the same type of model are being compared using the same dataset. This is a typical back-to-back testing situation.
B . Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for the same data.
This option involves comparing a custom implementation with a standard implementation, which is also a typical back-to-back testing scenario to validate the custom model against a known benchmark.
C . Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
This option involves comparing two different types of models (a neural network and a decision tree). This is not a typical scenario for back-to-back testing because the models are inherently different and would not be expected to produce identical results even on the same data.
D . Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
This option involves comparing the outputs of the same model on slightly different datasets. This could be seen as a form of robustness testing or sensitivity analysis, but not typical back-to-back testing as it doesn't involve comparing multiple implementations.
Based on this analysis, option C is the one that describes a situation of back-to-back testing the least because it compares two fundamentally different models, which is not the intent of back-to-back testing.

154. Frage
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