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ISTQB CT-AI Exam Overview:

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

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ISTQB CT-AI Prüfungsplan:

Thema	Einzelheiten
Thema 1	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Thema 2	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Thema 3	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 4	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 5	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 6	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 7	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 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	systems from those required for conventional systems.
Thema 10	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.

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

78. Frage
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?

A. Run the test several times to generate a statistically valid test result to ensure that an appropriate number of answers are accurate.
B. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting precise and accurate input data.
C. Run the test several times to ensure that the AI always returns the same correct test result.
D. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting a sufficient volume of input data.

Antwort: A

Begründung:
Probabilistic and non-deterministic AI-based systems do not always produce the same output for identical inputs. This makes traditional testing approaches ineffective. Instead, the best approach is to run tests multiple times and analyze results statistically. Statistical Validity: Running tests multiple times ensures that observed results are statistically significant. Instead of relying on a single test run, analyzing multiple iterations helps determine trends, probabilities, and outliers.
Expected Result Tolerance: AI-based systems may produce different results within an acceptable range. Defining acceptable tolerances (e.g., "result must be within 2% of the optimal value") improves test effectiveness.

79. Frage
Which assignment of AI techniques to testing support is BEST?
Choose ONE option (1 out of 4)

A. Fuzzy logic for the generation of test cases
B. Probabilistic methods for the prediction of system failures
C. Computational optimization techniques for defect prediction
D. Classification for the optimization of regression test cases

Antwort: B

Begründung:
The ISTQB CT-AI syllabus (Section5.2 - AI for Testing) explains that various AI approaches can support testing activities. Probabilistic methods-one of the three major AI technique groups-are used topredict system failures, especially when dealing with uncertainty, likelihood estimation, and reliability analysis. This aligns precisely with OptionB.
Option A is incorrect because regression test optimization is typically performed usingsearch-based optimization, not classification. Option C is incorrect because fuzzy logic is more suited to reasoning under vagueness, not generating test cases. Option D is incorrect: defect prediction relies on statistical learning or classification models, not computational optimization.
Thus,Option Bis the most syllabus-consistent mapping of AI techniques to testing tasks.

80. Frage
Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?
SELECT ONE OPTION

A. The challenge of providing explainability to the decisions made by the system.
B. Challenges resulting from low accuracy of the models.
C. The challenge of mimicking undefined scenarios generated due to self-learning
D. Challenges in the creation of scenarios of human handover for autonomous systems.

Antwort: D

Begründung:
AI test environments have several unique characteristics that differentiate them from traditional test environments. Let's evaluate each option:
A . Challenges resulting from low accuracy of the models.
Low accuracy is a common challenge in AI systems, especially during initial development and training phases. Ensuring the model performs accurately in varied and unpredictable scenarios is a critical aspect of AI testing.
B . The challenge of mimicking undefined scenarios generated due to self-learning.
AI systems, particularly those that involve machine learning, can generate undefined or unexpected scenarios due to their self-learning capabilities. Mimicking and testing these scenarios is a unique challenge in AI environments.
C . The challenge of providing explainability to the decisions made by the system.
Explainability, or the ability to understand and articulate how an AI system arrives at its decisions, is a significant and unique challenge in AI testing. This is crucial for trust and transparency in AI systems.
D . Challenges in the creation of scenarios of human handover for autonomous systems.
While important, the creation of scenarios for human handover in autonomous systems is not a characteristic unique to AI test environments. It is more related to the operational and deployment challenges of autonomous systems rather than the intrinsic technology-related characteristics of AI .
Given the above points, option D is the correct answer because it describes a challenge related to operational deployment rather than a technology-related characteristic unique to AI test environments.

81. Frage
A team of software testers is attempting to create an AI algorithm to assist in software testing. This particular team has gone through over 40 iterations of testing and cannot afford to spend as much time as it takes to run the full regression test suite. They are hoping to have the algorithm reduce the amount of testing required, thus reducing the time needed for each testing cycle.
How can an AI-based tool be expected to assist in this reduction?

A. By using a clustering method to quantify the relationships between test cases and then assigning each test case to a category
B. By performing Bayesian analysis to estimate the types of human interactions that are expected to be seen in the system and then selecting those test cases
C. By using A/B testing to compare the last update with the newest change and compare metrics between the two
D. By performing optimization of the data from past iterations to see where the most common defects occurred and select the corresponding test cases

Antwort: D

Begründung:
The syllabus mentions that AI can help optimize regression test suites:
"An AI-based tool can perform optimization of the regression test suite by analyzing... the information from previous test results, associated defects, and the latest changes that have been made, such as features which are broken more frequently and which tests exercise code impacted by recent changes." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.4, page 79 of 99)

82. Frage
Which of the following is a technique used in machine learning?

A. Equivalence partitioning
B. Boundary value analysis
C. Decision trees
D. Decision tables

Antwort: C

Begründung:
Decision trees are a widely usedmachine learning (ML) techniquethat falls undersupervised learning. They are used for bothclassification and regressiontasks and are popular due to their interpretability and effectiveness.
* How Decision Trees Work:
* The model splits the dataset into branches based on feature conditions.
* It continues to divide the data until each subset belongs to a single category (classification) or predicts a continuous value (regression).
* The final result is a tree structure where decisions are made atnodes, and predictions are given at leaf nodes.
* Common Applications of Decision Trees:
* Fraud detection
* Medical diagnosis
* Customer segmentation
* Recommendation systems
* B (Equivalence Partitioning):This is asoftware testing technique, not a machine learning method. It is used to divide input data into partitions to reduce test cases while maintaining coverage.
* C (Boundary Value Analysis):Anothersoftware testing technique, used to check edge cases around input boundaries.
* D (Decision Tables):A structuredtesting techniqueused to validate business rules and logic, not a machine learning method.
* ISTQB CT-AI Syllabus (Section 3.1: Forms of Machine Learning - Decision Trees)
* "Decision trees are used in classification and regression models and are fundamental ML algorithms".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Sincedecision trees are a core technique in machine learning, while the other options are software testing techniques, thecorrect answer is A.

83. Frage
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