ISTQB Certified Tester AI Testing : CT-AI

CT-AI real exams

Exam Code: CT-AI

Exam Name: Certified Tester AI Testing Exam

Updated: Nov 07, 2026

Q & A: 162 Questions and Answers

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About ISTQB Certified Tester AI Testing : CT-AI Exam

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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • systems from those required for conventional systems.
Topic 2
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 3
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 4
  • 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 5
  • 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 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
  • 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.
Topic 8
  • 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 9
  • 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 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.

Reference: https://www.istqb.org/certifications/artificial-inteligence-tester

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ISTQB Certified Tester AI Testing Sample Questions:

Question #1

Which statement about automation bias is correct?
Choose ONE option (1 out of 4)

  • A. Automation bias particularly affects testing of autonomous systems
  • B. Automation bias affects the testing of AI-based systems that support users in their actions or decisions
  • C. When testing AI-based systems, automation bias does not play a role in supporting test activities such as boundary value analysis
  • D. Automation bias is tested with representative users, but human input quality is irrelevant
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #2

Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION

  • A. Clustering is classification of a continuous quantity.
  • B. Clustering is supervised learning.
  • C. Clustering requires you to know the classes.
  • D. Clustering is done without prior knowledge of output classes.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #3

Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
SELECT ONE OPTION

  • A. The data pipeline
  • B. Biased data
  • C. The number of classes
  • D. The quality of the labeling
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #4

Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?
SELECT ONE OPTION

  • A. Validation data - test data
  • B. Training data - validation data
  • C. Training data * test data
  • D. Training data - validation data - test data
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #5

Which statement regarding data preparation in the ML workflow is correct?
Choose ONE option (1 out of 4)

  • A. A key challenge in data transformation is the removal or correction of erroneous data.
  • B. One challenge of data gathering is obtaining high-quality data from multiple sources.
  • C. Sampling is so well researched that it is no longer considered risky.
  • D. Since data preparation is time-consuming, all steps should be automated.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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