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ISTQB CT-AI Exam Overview:

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

TopicDetails
Topic 1
  • 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 2
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 3
  • systems from those required for conventional systems.
Topic 4
  • 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 5
  • 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 6
  • 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 7
  • 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 8
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
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
  • 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 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 (Q14-Q19):

NEW QUESTION # 14
Which ONE of the following is the BEST option to optimize the regression test selection and prevent the regression suite from growing large?

Answer: A

Explanation:
This is the most effective approach as AI-based tools can analyze historical test data, identify patterns, and prioritize tests that are more likely to catch defects based on past results. This method ensures an optimized and manageable regression test suite by focusing on the most impactful test cases.


NEW QUESTION # 15
Data used for an object detection ML system was found to have been labelled incorrectly in many cases.
Which ONE of the following options is most likely the reason for this problem?
SELECT ONE OPTION

Answer: B

Explanation:
The question refers to a problem where data used for an object detection ML system was labelled incorrectly. This issue is most closely related to "accuracy issues." Here's a detailed explanation:
Accuracy Issues: The primary goal of labeling data in machine learning is to ensure that the model can accurately learn and make predictions based on the given labels. Incorrectly labeled data directly impacts the model's accuracy, leading to poor performance because the model learns incorrect patterns.
Why Not Other Options:
Security Issues: This pertains to data breaches or unauthorized access, which is not relevant to the problem of incorrect data labeling.
Privacy Issues: This concerns the protection of personal data and is not related to the accuracy of data labeling.
Bias Issues: While bias in data can affect model performance, it specifically refers to systematic errors or prejudices in the data rather than outright incorrect labeling.


NEW QUESTION # 16
Which AI-specific test objective and acceptance criterion should be selected MOST LIKELY for testing GPT_Legal?
Choose ONE option (1 out of 4)

Answer: B

Explanation:
The ISTQB CT-AI syllabus introducesAI-specific quality characteristics, includingevolution,functional safety, compatibility, andbias-related data quality. Section5.1 - AI-Specific Test Objectivesexplains thatevolutionrefers to an AI system's capability to continue improving or at least maintain performance as it undergoes additional training. GPT_Legal is explicitly described as aself-learning systemexpected to:
continuously reduce false positives,
achieve weekly accuracy improvements of 10%,
reach and maintain 90% accuracy,
adapt to new environments (patent law firm # corporate legal department).
This aligns perfectly with the syllabus definition ofevidence of evolution: ensuring the model doesnot degradeas additional training data is introduced. OptionBtherefore directly supports the described acceptance criteria for this evolving, self-learning application.
Option A (functional safety) is irrelevant because patent searching and drafting do not constitute safety- critical domains. Option C (compatibility) is necessary but not the primary AI-specific objective. Option D addresses bias, which is important but not central to the described performance and continuous-learning expectations.
Thus,Option Bis the most appropriate AI-specific test objective.


NEW QUESTION # 17
An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases. Which of the following factors is necessary for a supervised machine learning algorithm to be successful?

Answer: B

Explanation:
The syllabus explains that supervised learning requires correctly labeled data so the algorithm can learn the relationship between input features and output labels:
"In supervised learning, the algorithm creates the ML model from labeled data during the training phase. The labeled data is used to infer the relationship between the input data and output labels."


NEW QUESTION # 18
Which ONE of the following statements BEST describes a testing challenge that specifically applied to a self-learning system?

Answer: D

Explanation:
A key challenge in testing self-learning systems is that, as the system learns and adapts over time, the results of previously passing tests may change. This is because the system's behavior evolves as it learns from new data, potentially altering how it responds to test inputs. This dynamic nature of self-learning systems makes it challenging to maintain consistent and reliable test results.


NEW QUESTION # 19
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