CT-AI Reliable Exam Pattern, CT-AI Detailed Study Dumps

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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 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.
Topic 2
  • 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 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
  • 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 5
  • 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 6
  • 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 7
  • systems from those required for conventional systems.
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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q108-Q113):

NEW QUESTION # 108
Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?
SELECT ONE OPTION

Answer: C

Explanation:
When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP). Here's why:
Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used to process textual requirements to extract relevant information and generate test cases. This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.
Why Not Other Options:
Analyzing source code for generating test cases: This is more suitable for white-box testing where the code is available, but it doesn't apply to text-based requirements.
Machine learning on logs of execution: This approach is used for dynamic analysis based on system behavior during execution rather than static textual requirements.
GUI analysis by computer vision: This is used for testing graphical user interfaces and is not applicable to text-based requirements.


NEW QUESTION # 109
A word processing company is developing an automatic text correction tool. A machine learning algorithm was used to develop the auto text correction feature. The testers have discovered that when they start typing
"Isle of Wight" it fills in "Isle of Eight". Several UAT testers have accepted this change without noticing.
What type of bias is this?

Answer: D

Explanation:
The syllabus describes automation bias as:
"A type of bias caused by a person favoring the recommendations of an automated decision-making system over other sources." This is also known as complacency bias, where testers accept automated system outputs without questioning them.
(Reference: ISTQB CT-AI Syllabus v1.0, Glossary, Page 89 of 99)


NEW QUESTION # 110
Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?
SELECT ONE OPTION

Answer: B

Explanation:
* A. Using a pixel comparison of the GUI before and after the change to check the differences.
Pixel comparison is a traditional method and does not involve AI . It compares images at the pixel level, which can be effective but is not an intelligent approach. It is not considered an AI usage and is the least likely usage of AI for detecting GUI changes.
* B. Using computer vision to compare the GUI before and after the test object changes.
Computer vision involves using AI techniques to interpret and process images. It is a likely usage of AI for detecting changes in the GUI .
* C. Using vision-based detection of the GUI layout changes before and after test object changes.
Vision-based detection is another AI technique where the layout and structure of the GUI are analyzed to detect changes. This is a typical application of AI .
* D. Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.
An ML-based classifier can intelligently determine significant changes and decide if they need human review, which is a sophisticated AI application.


NEW QUESTION # 111
Which of the following aspects is a challenge when handling test data for an AI-based system?

Answer: D

Explanation:
Handlingtest datain AI-based systems presents numerous challenges, particularly in terms ofdata privacy and confidentiality. AI models often require vast amounts of training data, some of which may containpersonal, sensitive, or confidential information. Ensuringcompliance with data protection laws (e.g., GDPR, CCPA)and implementingsecure data-handling practicesis a major challenge in AI testing.
* Data Privacy Regulations
* AI-based systems frequently process personal data, such as images, names, and transaction details, leading toprivacy concerns.
* Compliance with regulations such asGDPR (General Data Protection Regulation)andCCPA (California Consumer Privacy Act)requiresproper anonymization, encryption, or redactionof sensitive data before using it for testing.
* Data Security Challenges
* AI models mayleak confidential informationif proper security measures are not in place.
* Protectingtraining and test data from unauthorized accessis crucial to maintainingtrust and compliance.
* Legal and Ethical Considerations
* Organizations mustobtain legal approvalbefore using certain datasets, especially those containinghealth records, financial data, or personally identifiable information (PII).
* Testers may need toemploy synthetic dataordata maskingtechniques to minimize exposure risks.
* (B) Output data or intermediate data#
* While analyzing output data is important, it does notpose a significant challengecompared to handlingpersonal or confidential test data.
* (C) Video frame speed or aspect ratio#
* These aretechnical challengesin processing AI models but do not fall underdata privacy or ethical considerations.
* (D) Data frameworks or machine learning frameworks#
* Choosing an appropriateML framework (e.g., TensorFlow, PyTorch)is important, but it is nota major challenge related to test data handling.
* Handling personal or confidential data is a critical challenge in AI testing"Personal or otherwise confidential data may need special techniques for sanitization, encryption, or redaction.Legal approval for use may also be required." Why is Option A Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, asdata privacy and confidentiality are major challenges when handling test data for AI-based systems.


NEW QUESTION # 112
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?

Answer: C


NEW QUESTION # 113
......

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