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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
Available Languages:	English
Exam Format:	Multiple Choice
Passing Score:	65%
Certificate Validity Period:	Lifetime
Real Exam Qty:	40
Related Certifications:	ISTQB Certified Tester Foundation Level (CTFL)
Exam Duration:	60 minutes
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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CT-AI Exam Dumps | CT-AI Reliable Study Guide

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 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.
Topic 4	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 5	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 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.
Topic 7	systems from those required for conventional systems.
Topic 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.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q64-Q69):

NEW QUESTION # 64
Which ONE of the following options for a test basis would give the LEAST coverage when using AI-based test generation?

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

Answer: B

Explanation:
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.

NEW QUESTION # 65
Which ONE of the following would be the MOST effective input to an AI-based defect prediction tool?

A. Commit size
B. Cyclomatic complexity
C. Lines of code changed
D. Developers associated with previous code changes

Answer: B

Explanation:
Cyclomatic complexity would be the most effective input to an AI-based defect prediction tool.
Cyclomatic complexity is a software metric that measures the complexity of a program's control flow, which is closely related to the likelihood of defects. Higher complexity generally indicates a higher probability of defects. This makes it a strong predictor of potential issues in the code, and thus a valuable input for defect prediction.

NEW QUESTION # 66
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION

A. Non-determinism
B. Robustness
C. High complexity
D. Self-learning

Answer: B

Explanation:
The question asks which characteristic is least likely to cause safety-related issues for an AI system. Let's evaluate each option:
* Non-determinism (A): Non-deterministic systems can produce different outcomes even with the same inputs, which can lead to unpredictable behavior and potential safety issues.
* Robustness (B): Robustness refers to the ability of the system to handle errors, anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.
* High complexity (C): High complexity in AI systems can lead to difficulties in understanding, predicting, and managing the system's behavior, which can cause safety-related issues.
* Self-learning (D): Self-learning systems adapt based on new data, which can lead to unexpected changes in behavior. If not properly monitored and controlled, this can result in safety issues.
References:
* ISTQB CT-AI Syllabus Section 2.8 on Safety and AI discusses various factors affecting the safety of AI systems, emphasizing the importance of robustness in maintaining safe operation.

NEW QUESTION # 67
Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?

A. Classifying muffin purchases based on the perceived attractiveness of their packaging
B. Estimating the expected purchase of cat food after a particularly successful ad campaign
C. Grouping individual fish together based on their types of fins
D. Associating shoppers with their shopping tendencies

Answer: D

Explanation:
The syllabus defines clustering as:
"Clustering: This is when the problem requires the identification of similarities in input data points that allows them to be grouped based on common characteristics or attributes. For example, clustering is used to categorize different types of customers for the purpose of marketing." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.2, page 26 of 99)

NEW QUESTION # 68
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. An old application with lots of defect records but a brand new development and test team
C. An old application with few critical defect records and many non-critical defect records
D. A new application which is in the early stages of the first test cycle

Answer: A

Explanation:
An old application where defect records are linked to failed tests and production incidents would provide the most valuable data for an ML model to determine the criticality of new defects. By using historical data of defects that are linked to actual issues in production or testing failures, the model can learn patterns and correlations between defects and their criticality, making it highly effective in predicting the criticality of new defects. This type of historical data provides the necessary context for accurate predictions.

NEW QUESTION # 69
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