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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)
Real Exam Qty:	40
Exam Price:	€180 - €250 (varies by region and provider)
Exam Duration:	60 (75 for non-native language)
Available Languages:	German, French, English, Spanish, Korean, Japanese, Chinese, Portuguese
Related Certifications:	ISTQB Certified Tester Testing with Generative AI (CT-GenAI) ISTQB Certified Tester Foundation Level (CTFL)
Certificate Validity Period:	Valid indefinitely (no expiration)
Exam Format:	Multiple-choice questions, 1-2 points per question
Recommended Training:	ISTQB Accredited Training Providers CT-AI Syllabus v2.0
Exam Registration:	iSQI Exam Registration ISTQB Official Registration Pearson VUE
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 Exam Simulations | CT-AI Most Reliable Questions

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 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.
Topic 2	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.
Topic 3	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 4	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 5	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 6	systems from those required for conventional systems.
Topic 7	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 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.
Topic 9	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 10	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 11	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.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q154-Q159):

NEW QUESTION # 154
In which ONE of the following situations would an ML model be MOST effective at determining the criticality of new defects?

A. An old application where defect records are linked to failed tests and production incidents
B. A new application which is in the early stages of the first test cycle
C. An old application with lots of defect records but a brand new development and test team
D. An old application with few critical defect records and many non-critical defect records

Answer: A

Explanation:
The correct answer is C . The CT-AI syllabus states that ML models trained on the features of the most critical defects can be used to identify defects most likely to cause system failures that account for a large proportion of reported defects. This requires meaningful historical defect data and reliable links between defect features and real outcomes.
Option C provides the strongest training basis because defect records are linked to failed tests and production incidents. These links create useful labels and outcome evidence for learning criticality. Option A lacks historical data because the application is new and early in its first test cycle. Option B has many records, but the brand-new team reduces the relevance of people- and assignment-related patterns, and the option does not indicate that records are connected to critical outcomes. Option D is weakened by class imbalance: few critical defects and many non-critical defects make it harder for the model to learn the minority critical class.
The most effective ML setting is therefore an old application with rich, outcome-linked historical data.
References/topics: CT-AI Syllabus Chapter 11, Section 11.2 "Using AI to Analyze Reported Defects."
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NEW QUESTION # 155
Which ONE of the below is MOST likely to be a challenge in ML data preparation?

A. The time and associated costs requited
B. Designing a reward function
C. Functional performance
D. Specification of acceptance criteria

Answer: A

Explanation:
The correct answer is A . The CT-AI syllabus identifies data preparation as one of the most resource-intensive parts of the ML workflow, stating that it uses an average of 43% of ML workflow effort. It also explains that data preparation forms part of the data pipeline and includes acquisition, pre-processing, feature engineering, and related exploratory analysis.
The syllabus lists several data preparation challenges, including the need for domain and data knowledge, difficulty obtaining high-quality data from multiple sources, difficulty automating a scalable and efficient production data pipeline, costs associated with data preparation, defects introduced into the data pipeline, and sample bias.
Option B, functional performance, is a result or evaluation concern for the ML model rather than a data preparation challenge itself. Option C, designing a reward function, is specifically a reinforcement learning challenge. Option D, specifying acceptance criteria, belongs primarily to understanding objectives and defining evaluation targets, not the data preparation activity.
References/topics: CT-AI Syllabus Chapter 4, Section 4.1 "Data Preparation as Part of the ML Workflow"; Section 4.1.1 "Challenges in Data Preparation."
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NEW QUESTION # 156
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?

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

Answer: B

Explanation:
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.

NEW QUESTION # 157
Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION

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

Answer: D

Explanation:
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 is C. Flexible AI systems allow for easier modification of the system as a whole.
Reference:
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.

NEW QUESTION # 158
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. The X-ray images that an AI system can analyze must be structurally different from X-ray images used in a conventional system.
B. The implementation of an AI system consists mainly of training data, whereas that of a conventional system consists of branches and loops.
C. An AI system independently determines patterns in X-rays during training; a conventional system requires a human to program in those patterns.
D. The results of analyzing an X-ray for lung cancer using an AI system are more understandable than with a conventional system.

Answer: C

Explanation:
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.

NEW QUESTION # 159
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