ISTQB CT-AI Zertifizierungsfragen & CT-AI Zertifikatsdemo

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ISTQB CT-AI Exam Overview:

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

>> ISTQB CT-AI Zertifizierungsfragen <<

CT-AI Aktuelle Prüfung - CT-AI Prüfungsguide & CT-AI Praxisprüfung

Die Lerntipps zur ISTQB CT-AI Prüfung von PrüfungFrage können ein Leuchtturm in Ihrer Karriere sein. Denn es enthält alle Prüfungsfragen und Antworten zur CT-AI Zertifizierung. Wählen Sie PrüfungFrage und es kann Ihnen helfen, die ISTQB CT-AI Prüfung zu bestehen. Das ist absolut eine weise Entscheidung. PrüfungFrage ist Ihr Helfer und Sie können bessere Resultate bei weniger Einsatz erzielen.

ISTQB CT-AI Prüfungsplan:

Thema	Einzelheiten
Thema 1	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 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	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 4	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
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	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 7	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 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.
Thema 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.
Thema 10	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Thema 11	systems from those required for conventional systems.

ISTQB Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q78-Q83):

78. Frage
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. Regression
C. Reinforcement learning
D. Classification

Antwort: D

Begründung:
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.

79. Frage
A transportation company operates three types of delivery vehicles in its fleet. The vehicles operate at different speeds (slow, medium, and fast). The transportation company is attempting to optimize scheduling and has created an AI-based program to plan routes for its vehicles using records from the medium-speed vehicle traveling to selected destinations. The test team uses this data in metamorphic testing to test the accuracy of the estimated travel times created by the AI route planner with the actual routes and times.
Which of the following describes the next phase of metamorphic testing?

A. The team decomposes each route into the relevant components that affect the travel time such as traffic density and vehicle power. The team then uses statistical analysis to characterize the influence of each component to calculate the fast and slow vehicle route times.
B. The team tests the time required for the fast and slow vehicles to travel the same route as the medium vehicle. Then, by calculating the speed difference, they then predict how much faster or slower the vehicles will travel. That information is then used to verify that the arrival time of the vehicles meets the expected result.
C. The team uses the same AI route planner to create routes that are longer and shorter but follow the same track. Finally, by driving the fast vehicles on the long routes and slow vehicles on the short routes and vice versa, the AI system will have enough information to infer travel times for all vehicles on all routes.
D. The team uses an AI system to select the most dissimilar routes. With this information, any of the AI routes can be metaphorically transformed into a fast or slow route.

Antwort: B

Begründung:
Metamorphic Testing (MT) is a testing technique that verifies AI-based systems by generating follow-up test cases based on existing test cases. These follow-up test cases adhere to a Metamorphic Relation (MR), ensuring that if the system is functioning correctly, changes in input should result in predictable changes in output.
Metamorphic testing works by transforming source test cases into follow-up test cases Here, the source test case involves testing the medium-speed vehicle's travel time. The follow-up test cases are derived by extrapolating travel times for fast and slow vehicles using predictable relationships based on speed differences.
MR states that modifying input should result in a predictable change in output Since the speed of the vehicle is a known factor, it is possible to predict the new arrival times and verify whether they follow expected trends.
This is a direct application of metamorphic testing principles In route optimization systems, metamorphic testing often applies transformations to speed, distance, or conditions to verify expected outcomes.

80. Frage
Which statement about using AI to analyze reported defects is MOST correct?

A. ML models trained with critical defect tickets can identify defects that cause serious consequences.
B. ML models identify developers who should handle a defect based on ticket content.
C. ML models can identify categories for a reported defect during assignment.
D. ML models can support duplicate defect identification when checking defect criticality.

Antwort: C

Begründung:
The ISTQB CT-AI syllabus (Section5.3 - AI Support for Defect Analysis) explains that AI can categorize defect reports using natural language processing or classification models.
Categorization helps route defects efficiently and determine which areas of the system are affected. Thus, Option C is correct: AI canidentify defect categories, supporting assignment and triage.

81. Frage
Which option describes a reasonable application of AIB testing for a self-learning system after it has changed its behavior due to user input?

A. Comparing outputs before and after the change using identical inputs
B. Comparing outputs of a non-self-learning system with those of the changed self-learning system
C. Comparing outputs before and after the change using different inputs
D. Generating test cases for the system before and after the change, since neither has a test oracle

Antwort: A

Begründung:
According to Section4.6 - AI Behaviour Testing (AIB Testing)of the ISTQB CT-AI syllabus, AIB testing is used to evaluate changes in the functional behavior of self-learning systems. The core principle iscomparing pre-change and post-change model behavior using the same test inputs, so that any difference in outputs can be attributed to the model's learning and not to differences in input data. This directly corresponds to Option C.

82. Frage
A system is to be developed to detect lung cancer using X-ray images.
Which statement BEST describes the difference between a conventional system and an AI system with supervised machine learning?
Choose ONE option (1 out of 4)

A. An AI system independently determines patterns in X-rays during training; a conventional system requires a human to program in those patterns.
B. The implementation of an AI system consists mainly of training data, whereas that of a conventional system consists of branches and loops.
C. The X-ray images that an AI system can analyze must be structurally different from X-ray images used in a conventional system.
D. The results of analyzing an X-ray for lung cancer using an AI system are more understandable than with a conventional system.

Antwort: A

Begründung:
The syllabus explains the fundamental distinction betweenconventional systemsandAI-based systems using supervised machine learningin Section1.3 - AI-Based and Conventional Systems. A conventional system relies on human-programmed logic-such as branches, conditions, and explicit rules-to interpret input data.
The system behaves exactly as specified by its developers.
In contrast,AI systems using supervised learning automatically learn patternsfrom labeled data. The syllabus states that"patterns in data are used by the system to determine how it should react in the future...
The AI determines on its own what patterns or features in the data can be used". This aligns directly with Option C: an AI system identifies relevant diagnostic patterns in X-ray images during training, whereas a conventional system requires human experts to explicitly program those patterns.
Option A is incorrect because AI outputs are typicallylessexplainable, not more. Option B is incorrect because both systems can use thesame X-ray images; ML does not require structurally different images. Option D is oversimplified and not fully accurate; while training data is central to ML, AI systems also include architecture, algorithms, and preprocessing-not just data.
Thus,Option Cis the correct and syllabus-aligned answer.

83. Frage
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