ISTQB CT-AI PDF Demo, CT-AI Deutsche Prüfungsfragen

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ISTQB CT-AI Prüfungsplan:

Thema	Einzelheiten
Thema 1	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 2	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
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	systems from those required for conventional systems.
Thema 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.
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	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 8	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 9	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.
Thema 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.
Thema 11	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.

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CT-AI Deutsche Prüfungsfragen & CT-AI Exam

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ISTQB Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q80-Q85):

80. Frage
Which of the following describes the AI effect?
Choose ONE option (1 out of 4)

A. The changing perception of what constitutes AI
B. The ability of AI to learn from data itself
C. The ability of AI to defeat a human, e.g., in chess
D. The fact that mankind can build intelligent machines

Antwort: A

Begründung:
TheAI Effectis clearly defined in theISTQB Certified Tester AI Testing Syllabus v1.0under Section1.1 - Definition of AI and AI Effect. The document explains that society's understanding of what qualifies as
"AI" changes over time. Technologies once considered AI-such as expert systems from the 1970s and 1980s or early chess-playing systems-are no longer viewed as AI today. This phenomenon is explicitly labeled the
"AI Effect,"described as"the changing perception of what constitutes AI."The syllabus states that as AI capabilities become routine or widely implemented, they often stop being perceived as true artificial intelligence .
Options B, C, and D do not capture this definition. While AI learning from data (B) is a property of ML, it does not describe the shifting perception of AI. Option C describes a technological achievement, not a perceptual shift. Option D references a historical AI milestone (Deep Blue defeating Kasparov) that the syllabus specifically uses as an example of technology that is no longer considered AI due to the AI Effect.
Therefore, onlyOption Aaccurately reflects the AI Effect as defined by the ISTQB syllabus.

81. Frage
A startup company has implemented a new facial recognition system for a banking application for mobile devices. The application is intended to learn at run-time on the device to determine if the user should be granted access. It also sends feedback over the Internet to the application developers. The application deployment resulted in continuous restarts of the mobile devices.
Which of the following is the most likely cause of the failure?

A. The size of the application is consuming too much of the phone's storage capacity
B. The feedback requires a physical connection and cannot be sent over the Internet
C. The training, processing, and diagnostic generation are too computationally intensive for the mobile device hardware to handle
D. Mobile operating systems cannot process machine learning algorithms

Antwort: C

Begründung:
The syllabus highlights that on-device training and processing require considerable computational power, which may exceed the capabilities of some mobile devices:
"Self-learning and continuous learning systems require large amounts of computational power, which can impact system performance and stability if the hardware is not powerful enough." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.3, page 22 of 99)

82. Frage
Which ONE of the below statements BEST describes how combinatorial testing can be applied to AI based systems?

A. Two variants of the system can be used and compared
B. Each neuron can be treated as a parameter for pairwise tests
C. Inputs to the system and environment factors can be considered parameters for pairwise tests
D. Combinatorial testing cannot yet be applied to AI

Antwort: C

Begründung:
The correct answer is C . The CT-AI syllabus explains that AI-based systems can have extremely high numbers of parameters, especially where they use big data or interact with the external world. Exhaustive testing of all parameter combinations is usually infeasible, so combinatorial techniques, especially pairwise testing, can reduce the number of required test cases while preserving useful defect detection capability.
The syllabus gives autonomous driving as an example, where high-level system tests should consider different operational environments and vehicle functions. Relevant parameters may include road types, surfaces, weather, traffic conditions, visibility, vehicle functions, and sensor effectiveness. This directly supports treating system inputs and environmental factors as parameters in pairwise tests.
Option A is not the intended application; neuron coverage is a neural-network coverage topic, not pairwise system scenario design. Option B describes A/B testing or back-to-back comparison, not combinatorial testing. Option D is false because the syllabus explicitly describes pairwise testing for AI-based systems and includes it as an examinable method.
References/topics: CT-AI Syllabus Chapter 9, Section 9.2 "Pairwise Testing."
=========

83. Frage
A mobile app start-up company is implementing an AI-based chat assistant for e-commerce customers. In the process of planning the testing, the team realizes that the specifications are insufficient.
Which testing approach should be used to test this system?

A. Static analysis
B. State transition testing
C. Equivalence partitioning
D. Exploratory testing

Antwort: D

Begründung:
Whentesting an AI-based chat assistantfor e-commerce customers, thelack of sufficient specifications makes it difficult to use structured test techniques. TheISTQB CT-AI Syllabusrecommendsexploratory testingin such cases:
* Why Exploratory Testing?
* Exploratory testing is usefulwhen specifications are incomplete or unclear.
* AI-based systems, particularly those usingnatural language processing (NLP),may not behave deterministically, making scripted test cases ineffective.
* Thetester interacts dynamicallywith the system, identifying unexpected behaviorsnot documented in the specification.
* Analysis of Answer Choices:
* A (Exploratory testing)#Correct, as it is the best approach when specifications are incomplete.
* B (Static analysis)# Incorrect, as static analysis checks code without execution, which isnot helpfulfor AI chatbots.
* C (Equivalence partitioning)# Incorrect, asthis technique requires well-defined inputs and outputs, which are missing due toinsufficient specifications.
* D (State transition testing)# Incorrect, as state-based testingrequires knowledge of valid and invalid transitions, which is difficult with a chatbot lacking a clear specification.
Thus,Option A is the correct answer, asexploratory testing is the best approach when dealing with insufficient specifications in AI-based systems.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 7.7 (Selecting a Test Approach for an ML System)
* ISTQB CT-AI Syllabus v1.0, Section 9.6 (Experience-Based Testing of AI-Based Systems).

84. Frage
Which of the following problems would best be solved using the supervised learning category of regression?

A. Determining if an animal is a pig or a cow based on image recognition
B. Determining the optimal age for a chicken's egg-laying production using input data of the chicken's age and average daily egg production for one million chickens
C. Recognizing a knife in carry-on luggage at a security checkpoint in an airport scanner
D. Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store

Antwort: B

Begründung:
The syllabus states:
"Supervised learning... divides problems into two categories: classification and regression. Regression is used when the problem requires the ML model to predict a numeric output, for example predicting the age of a person based on their habits." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1, Page 26 of 99)

85. Frage
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